N=2 Superstrings with (1,2m) Spacetime Signature

We show that the $N=2$ superstring in $d=2D\ge6$ real dimensions, with criticality achieved by including background charges in the two real time directions, exhibits a ``coordinate-freezing'' phenomenon, whereby the momentum in one of the two time directions is constrained to take a specific value for each physical state. This effectively removes this time direction as a physical coordinate, leaving the theory with $(1,d-2)$ real spacetime signature. Norm calculations for low-lying physical states suggest that the theory is ghost free.Comment: 8 page

Temperature effects on growth, colony development and carbon partitioning in three <i>Phaeocystis</i> species

Phaeocystis is an ecologically important marine phytoplankton genus that is globally distributed. We examined the effects of temperature on the 3 most common species: P. globosa, P. antarctica, and P. pouchetii, which grew at 16-32, 0-6, and 4-8 ° C, respectively. P. pouchetii did not form colonies; P. globosa formed colonies at 16, 20, and 24 ° C, and P. antarctica colonies were observed at all temperatures. More cells were partitioned into the colonial form at lower temperatures than at higher temperatures for P. globosa and P. antarctica. P. globosa colony size decreased with temperature, whereas P. antarctica colony size showed no distinct response to temperature. Numbers of cells per unit of colony surface area of P. globosa and P. antarctica were lowest at temperatures where highest growth rates and colonial abundances were observed; more organic carbon was partitioned into solitary cell biomass at higher temperatures, whereas the carbon concentration of colonies was not affected by temperature. Maximum quantum yield of P. antarctica and P. globosa exhibited subtle responses to temperature, whereas that of P. pouchetii was relatively invariant within the growth temperature range. Future changes in sea surface temperature may dramatically alter the ecology and biogeochemical cycles of systems dominated by Phaeocystis spp. and result in further degradation, via oxygen depletion and altered food web structure

Cyclic gate recurrent neural networks for time series data with missing values

Gated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been highly effective in handling sequential time series data in recent years. Although Gated RNNs have an inherent ability to learn complex temporal dynamics, there is potential for further enhancement by enabling these deep learning networks to directly use time information to recognise time-dependent patterns in data and identify important segments of time. Synonymous with time series data in real-world applications are missing values, which often reduce a model’s ability to perform predictive tasks. Historically, missing values have been handled by simple or complex imputation techniques as well as machine learning models, which manage the missing values in the prediction layers. However, these methods do not attempt to identify the significance of data segments and therefore are susceptible to poor imputation values or model degradation from high missing value rates. This paper develops Cyclic Gate enhanced recurrent neural networks with learnt waveform parameters to automatically identify important data segments within a time series and neglect unimportant segments. By using the proposed networks, the negative impact of missing data on model performance is mitigated through the addition of customised cyclic opening and closing gate operations. Cyclic Gate Recurrent Neural Networks are tested on several sequential time series datasets for classification performance. For long sequence datasets with high rates of missing values, Cyclic Gate enhanced RNN models achieve higher performance metrics than standard gated recurrent neural network models, conventional non-neural network machine learning algorithms and current state of the art RNN cell variants

An Empirical Study on the Logistics Service Quality of Online Shopping Business in China

This study aims to find out the differences between customer expectations and customer experiences on logistics service quality (LSQ) of online shopping in China. Data was collected from 153 respondents through an online shopping site. Structural equation modeling is used to examine the reliability and validity of the research model. This study indicates that order condition and order discrepancy handling are the most important LSQ areas that logistics service providers should address in order to improve their service quality as well as to strength their business development

Cold Induction of EARLI1, a Putative Arabidopsis Lipid Transfer Protein, Is Light and Calcium Dependent

As sessile organisms, plants must adapt to their environment. One approach toward understanding this adaptation is to investigate environmental regulation of gene expression. Our focus is on the environmental regulation of EARLI1, which is activated by cold and long-day photoperiods. Cold activation of EARLI1 in short-day photoperiods is slow, requiring several hours at 4ºC to detect an increase in mRNA abundance. EARLI1 is not efficiently cold-activated in etiolated seedlings, suggesting that photomorphogenesis is necessary for its cold activation. Cold activation of EARLI1 is inhibited in the presence of the calcium channel blocker lanthanum chloride or the calcium chelator EGTA. Addition of the calcium ionophore Bay K8644 results in cold-independent activation of EARLI1. These data suggest that EARLI1 is not an immediate target of the cold response, and that calcium flux affects its expression. EARLI1 is a putative secreted protein and has motifs found in lipid transfer proteins. Over-expression of EARLI1 in transgenic plants results in reduced electrolyte leakage during freezing damage, suggesting that EARLI1 may affect membrane or cell wall stability in response to low temperature stress

PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique

The performance of classification algorithms with highly imbalanced streaming data depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied using static batch data to resolve the class imbalance problem, which is difficult if applied for massive data streams. In this paper, a new Piece-Wise Incremental Data re-Balancing (PWIDB) framework is proposed. The PWIDB framework combines automated balancing techniques using Racing Algorithm (RA) and incremental rebalancing technique. RA is an active learning approach capable of classifying imbalanced data and can provide a way to select an appropriate re-balancing technique with imbalanced data. In this paper, we have extended the capability of RA for handling imbalanced data streams in the proposed PWIDB framework. The PWIDB accumulates previous knowledge with increments of re-balanced data and captures the concept of the imbalanced instances. The PWIDB is an incremental streaming batch framework, which is suitable for learning with streaming imbalanced data. We compared the performance of PWIDB with a well-known FLORA technique. Experimental results show that the PWIDB framework exhibits an improved and stable performance compared to FLORA and accumulative re-balancing techniques

Anomaly Freedom and Realisations for Super-W3W_3 Strings

We construct new multi-field realisations of the $N=2$ super-$W_3$ algebra, which are important for building super-$W_3$ string theories. We derive the structure of the ghost vacuum for such theories, and use the result to calculate the intercepts. These results determine the conditions for physical states in the super-$W_3$ string theory.Comment: 22 page

Domain Wall Resistance in Perpendicular (Ga,Mn)As: dependence on pinning

We have investigated the domain wall resistance for two types of domain walls in a (Ga,Mn)As Hall bar with perpendicular magnetization. A sizeable positive intrinsic DWR is inferred for domain walls that are pinned at an etching step, which is quite consistent with earlier observations. However, much lower intrinsic domain wall resistance is obtained when domain walls are formed by pinning lines in unetched material. This indicates that the spin transport across a domain wall is strongly influenced by the nature of the pinning.Comment: 9 pages, 3 figure

Effects of free surface and heterogeneous residual internal stress on stress-driven grain growth in nanocrystalline metals

By reevaluating the experimental study of Zhang et al. (2005), here we demonstrate that the extent of grain growth, previously proposed to be solely driven by external stress, may have been significantly overestimated. A new physical mechanism, termed as free surface assisted stress-driven grain growth (or self-mechanical annealing), is proposed and discussed in detail. Representing the cooperative effect of free surface and heterogeneous residual internal stress, the proposed mechanism is considered more favorable than the traditional pure stress-driven mechanism for interpreting the abnormal grain growth widely observed in deforming nanocrystalline metals at room temperature