Researching Dynamic Brand Competitiveness Based on Consumer Clicking Behavior

Analyzing brand dynamic competition relationship by using consumer sequential online click data, which was collected from JD.com. It is found that the competition intensity of the products across categories is quite different. Owing to the purchasing time of durable-like goods is more flexible, that is, the purchasing probability of such products changes more obviously over time. Therefore, we use the Local Polynomial Regression Model to analyze the relationship between the brand competition of durable-like goods and the purchasing probability of the specific brand. Finding that when brands increase at a half of the total market share for consumers cognition preference, the brands’ competitiveness is peak and makes no significant different from one hundred percent for consumer to complete a transaction. The findings contribute to brand competitiveness for setting up marketing strategy from the dynamic and online consumer behavior’s perspective

An Optimal Method For Product Selection By Using Online Ratings And Considering Search Costs

With the collecting and publishing data about consumers purchasing and browsing products at the platform of online, this data prodives new ways to better understand the consumers search behavior before purchase. How to base on consumers online search behavior and simutaneously consider offline experience costs is worth studying. An optimal method based on the utility of the attribute of product is proposed. The proposed method follows steps below. Firstly, based on the multi-attribute utility theory, the overall utility of product is calculated by using ratings data. Secondly, the overall utility is combined into the original sequential search model to find the optimal selection strategy. Thirdly, the candidate product sets arranged in descending order of the reservation utilities are finally obtained. Finally, taking the online ratings data provided by a comprehensive automobile website as an example, lastly the proposed method is simulated and compared with other method. The result shows that the proposed method is feasible and effective

Optimal Management of DC Pension Plan with Inflation Risk and Tail VaR Constraint

This paper investigates an optimal investment problem under the tail Value at Risk (tail VaR, also known as expected shortfall, conditional VaR, average VaR) and portfolio insurance constraints confronted by a defined-contribution pension member. The member's aim is to maximize the expected utility from the terminal wealth exceeding the minimum guarantee by investing his wealth in a cash bond, an inflation-linked bond and a stock. Due to the presence of the tail VaR constraint, the problem cannot be tackled by standard control tools. We apply the Lagrange method along with quantile optimization techniques to solve the problem. Through delicate analysis, the optimal investment output in closed-form and optimal investment strategy are derived. A numerical analysis is also provided to show how the constraints impact the optimal investment output and strategy

Tetrakis[μ-3-(3-pyridyl)acrylato-κ2 O:O′]bis{(1,10-phenanthroline-κ2 N,N′)[3-(3-pyridyl)acrylato-κ2 O,O′]europium(III)} pentahydrate

The europiumIII ion in the title compound, [Eu2(C8H6NO2)6(C12H8N2)2]·5H2O, is coordinated by seven carboxyl­ate O atoms and two N atoms from one phenanthroline mol­ecule. The carboxyl­ate groups of 3-(3-pyrid­yl)acrylate link pairs of europium(III) ions, forming centrosymmetric dinuclear units, which further assemble into a sheet parallel to the (001) plane through hydrogen-bonding inter­actions involving the uncoordinated water mol­ecules. One water molecule is disordered

Spin transfer and polarization of antihyperons in lepton induced reactions

We study the polarization of antihyperon in lepton induced reactions such as $e^+e^-\to\bar H+X$ and $l+p\to l'+\bar H+X$ with polarized beams using different models for spin transfer in high energy fragmentation processes. We compare the results with the available data and those for hyperons. We make predictions for future experiments.Comment: 31 pages, 6 figures. submitted to Phys. Rev. D. content changed, references adde

Self Sparse Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem. In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem. In the Self-Sparse GAN, we design a Self-Adaptive Sparse Transform Module (SASTM) comprising the sparsity decomposition and feature-map recombination, which can be applied on multi-channel feature maps to obtain sparse feature maps. The key idea of Self-Sparse GAN is to add the SASTM following every deconvolution layer in the generator, which can adaptively reduce the parameter space by utilizing the sparsity in multi-channel feature maps. We theoretically prove that the SASTM can not only reduce the search space of the convolution kernel weight of the generator but also alleviate the zero gradient problem by maintaining meaningful features in the Batch Normalization layer and driving the weight of deconvolution layers away from being negative. The experimental results show that our method achieves the best FID scores for image generation compared with WGAN-GP on MNIST, Fashion-MNIST, CIFAR-10, STL-10, mini-ImageNet, CELEBA-HQ, and LSUN bedrooms, and the relative decrease of FID is 4.76% ~ 21.84%

How Neutral is the Intergalactic Medium at z ~ 6?

Recent observations of high redshift quasar spectra reveal long gaps with little flux. A small or no detectable flux does not by itself imply the intergalactic medium (IGM) is neutral. Inferring the average neutral fraction from the observed absorption requires assumptions about clustering of the IGM, which the gravitational instability model supplies. Our most stringent constraint on the neutral fraction at z ~ 6 is derived from the mean Lyman-beta transmission measured from the z=6.28 SDSS quasar of Becker et al. -- the neutral hydrogen fraction at mean density has to be larger than 4.7 times 10^{-4}. This is substantially higher than the neutral fraction of ~ 3-5 times 10^{-5} at z = 4.5 - 5.7, suggesting that dramatic changes take place around or just before z ~ 6, even though current constraints are still consistent with a fairly ionized IGM at z ~ 6. An interesting alternative method to constrain the neutral fraction is to consider the probability of having many consecutive pixels with little flux, which is small unless the neutral fraction is high. This constraint is slightly weaker than the one obtained from the mean transmission. We show that while the derived neutral fraction at a given redshift is sensitive to the power spectrum normalization, the size of the jump around z ~ 6 is not. We caution that systematic uncertainties include spatial fluctuations in the ionizing background, and the continuum placement. Tests are proposed. In particular, the sightline to sightline dispersion in mean transmission might provide a useful diagnostic. We express the dispersion in terms of the transmission power spectrum, and develop a method to calculate the dispersion for spectra that are longer than the typical simulation box.Comment: 20 pages, 5 figures; ApJ accepted version; constraints revised due to a revised power spectrum normalization in fiducial mode