Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network

Deep neural network has proven to be very effective in computer vision fields.Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods.Generative adversarial network(GAN)is becoming one of the highlights among these deep neural networks.GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks.One promising application of using GAN generated images would be image concealing which requires the embedded image looks like not being tampered to human vision system and also undetectable to most analyzers.Texture synthesizing has drawn lots of attention in computer vision field and is used for image concealing in steganography and watermark.The traditional methods which use synthesized textures for information hiding mainly select features and mathematic functions by human metrics and usually have a low embedding rate.This paper takes advantage of the generative network and proposes an approach for synthesizing complex texture-like image of arbitrary size using a modified deep convolutional generative adversarial network(DCGAN),and then demonstrates the feasibility of embedding another image inside the generated texture while the difference between the two images is nearly invisible to the human eyes.

Analysis on the Advantages and Disadvantages of Using Multimedia Courseware in Chinese Folk Dance Classes

The development of technology will affect the development of traditional dance classes.The teaching method is no longer a singular course of teaching and learning.Multimedia courseware is a commonly used course technology,based on computer and projection technology,to convey rich knowledge to students.This paper discusses the use of multimedia courseware in dance classes,then,combined with the majors the author have studied,creatively discusses the influence of multimedia courseware on Chinese folk dance courses.

Pigs fed camelina meal increase hepatic gene expression of cytochrome 8b1, aldehyde dehydrogenase, and thiosulfate transferase

Comelina sotivo is an oil seed crop which can be grown on marginal lands. Camelina seed oil is rich in omega-3 fatty acids （〉35%） and y-tocopherol but is also high in erucic acid and glucosinolates. Camelina meal, is the by-product after the oil has been extracted. Camelina meal was fed to 28 d old weaned pigs at 3.7% and 7.4% unt age 56 d. The camelina meal supplements in the soy based diets, improved feed efficiency but also significantly increased the liver weights. Gene expression analyses of the livers, using intra-species microarrays, identified increased expression of phase 1 and phase 2 drug metabolism enzymes. The porcine versions of the enzymes were confirmed by real time PCR. Cytochrome 8b1 （CYPSB1）, aldehyde dehydrogenase 2 （Aldh2）, and thiosulfate transferase （TST） were all significantly stimulated. Collectively, these genes implicate the camelina glucosinolate metabolite, methyl-sulfinyldecyl isothiocyanate, as the main xeniobiotic, causing increased hepatic metabolism and increased liver weight.

Missing the Target?—Targeted Therapy in Small Cell Lung Cancer

Small cell lung cancer [SCLC] is a devastating form of cancer, with most patients harbouring extensive disease at diagnosis and survival of less than 5% at five years. Progress in novel therapies has been limited. This specialist review explores current targeted therapy options and potential areas of development.

Flicker defined form and RareBit measurements lack in specificity of visual pathways

AIM:To evaluate the different visual pathways represented by the Heidelberg Engineering Perimeter flicker defined form and RareBit(magnocellular and parvocellular respectively)in different age-groups and according to the fatigue.METHODS:Totally 64 eyes of 32 healthy subjects were included in the prospective study.Each participant underwent screening—ophthalmic examination including best-corrected visual acuity,anterior and posterior segment assessment,and visual field examination with Heidelberg Edge Perimetry(HEP)-standard automated perimeter(SAP)24-2.They were observed for 2 y previously to the enrollment.This helped to define that the enrolled patients did not bear the glaucoma-developing potential.During the screening and after two years the HEP had been conducted in the standard protocol 24-2 and RareBit perimetry(RBP)in accordance with the manufacturer’s description.Participants were randomly assigned to the groups:flicker defined form(FDF)-first or RBP-first.This defined from which additional visual field test the participant started.Participants were additionally subdivided to younger and older study groups.The effect of subject variables was explored with Mann-Whitney U-test.Testing for the presence of correlations between parameters was performed using the Spearman Rank Order Correlations and confirmed by the parametric tests.For the influence of additional factors,the Kruskal-Wallis test was performed.RESULTS:The positive correlation between meandeviation(MD)and mean hit rate(MHR)and pattern standard deviation(PSD)and standard deviation of MHR(±MHR)were found in younger study group(P=0.005,r=0.481 and P=0.0074,r=0.465),whereas in the older subgroup no correlation was observed.Additionally,the randomization protocol helped in defining the role of fatigue on the HEP-FDF results.Participant for whom the HEPFDF was performed after RareBit had significantly worse results than those for whom the HEP-FDF was first.In the younger group,the MHR and±MHR depend from age in that group(P<0.05,r=0.43 and r=-0.57 respectively)while no age-dependent differences were found in HEPrelated parameters.On the contrary in the older group the MD and PSD varies with age(P<0.05,r=0.47 and r=-0.44 respectively)while the RBP parameters remained unchanged.The questionnaire showed that participants preferred RareBit over HEP-FDF in terms of a duration time,comfort,understanding of the test procedures,and ocular pain(P<0.05).CONCLUSION:The influence of patient’s fatigue should be considered during HEP-FDF examination.An overlap hypothesis should be reevaluated after determining of other factors that affect HEP-FDF and RareBit results.

PRAIRIE-BASED GREEN ROOFS:LITERATURE,TEMPLATES,AND ANALOGS

Native prairie species have been both promoted and questioned in their ability to serve as vegetative covers for green roofs.The green roof environment with its exposure to intense sun and wind and limited moisture restricts the capacity for a large diversity of species.The result has been,in many cases,a standard,low-diversity mix of Sedum species often focused on ornament and minimizes the potential for wider environmental benefits.We reviewed the ecological literature on prairie and grassland communities with specific reference to habitat templates from stressed environmental conditions and examined analogs of prairie-based vegetation on twenty-one existing green roofs.We found that many,but not all prairie and grassland species will survive and thrive on green roofs,especially when irrigated as needed or given adequate growing medium depth.We raise several important questions about media,irrigation,temperature,biodiversity and their interactions needing more study.

Hybrid Control Strategy for Air-conditioning Loads Participating in Peak Load Reduction Through Wide-range Transport Model

This paper proposes a hybrid control strategy of air-conditioning loads(ACLs)for participating in peak load reduction.The hybrid control strategy combines the temperature setpoint adjustment(TSA)control and on/off control together to make full use of response potentials of ACLs.The primary free transport model of ACLs has been established in literature at or near a fixed temperature setpoint.In this paper,a wide-range transport(WRT)model suitable for larger value of TSA is proposed.The WRT model can be constructed easily through the parameter of devices and indoor and outdoor temperature.To modulate the aggregate response characteristics of ACLs more friendly to the power grid,the safe protocol(SP)is adopted and integrated into the WRT model,which achieves a good unification of oscillation suppression and efficient modeling.Moreover,the hybrid control strategy is implemented based on the WRT model,and the model predictive control(MPC)controller is designed considering the tracking error and control switch cost.At last,the superiority of the hybrid control strategy is verified and the performance of ACLs for peak load reduction under this controller is simulated.The simulation results show that the hybrid control strategy could exploit the load reduction potential of ACLs fully than the TSA mode and track the reference signal more accurately.

Probabilistic Tractable Models in Mixed Discrete-Continuou s Domains

We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains.The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging,compounded by the fact that inference is computationally demanding.The situation is generally believed to be significantly worse in discrete-continuous domains:estimating the unknown probability distribution of given samples is often limited in practice to a handful of parametric forms,and in addition to that,computing conditional queries need to carefully handle low-probability regions in safety-critical applications.In recent years,the regime of tractable learning has emerged,which attempts to learn a graphical model that permits efficient inference.Most of the results in this regime are based on arithmetic circuits,for which inference is linear in the size of the obtained circuit.In this work,we show how,with minimal modifications,such regimes can be generalized by leveraging efficient density estimation schemes based on piecewise polynomial approximations.Our framework is realized on a recent computational abstraction that permits efficient inference for a range of queries in the underlying language.Our empirical results show that our approach is effective,and allows a study of the trade-off between the granularity of the learned model and its predictive power.