The trip to the supermarket is one of the most basic elements of consumer behavior (Bawa and Gosh, 1999). In geography, the traditional belief according to Christaller's central place theory is that accessibility is the most important explanatory variable in grocery shopping, as consumers tend to visit more-conveniently located stores for loworder goods. Thus, grocery stores generally have a low threshold and small market range, and grocery shopping is often regarded as a local activity. Concerns about the critical role of accessibility arise when we consider the immigrant shopping experience in multicultural cities such as Toronto and Los Angeles, which have experienced dramatic growth in ethnic economies consisting of businesses owned and operated by members of ethnic-minority groups (
Several researchers have demonstrated that the virtual behaviors committed in a video game can elicit feelings of guilt. Researchers have proposed that such guilt could have prosocial consequences. However, this proposition has not been supported with empirical evidence. The current study examined this issue in a 2 · 2 (video game play vs. real world recollection · guilt vs. control) experiment. Participants were first randomly assigned to either play a video game or complete a memory recall task. Next, participants were randomly assigned to either a guilt-inducing condition (game play as a terrorist/recall of acts that induce guilt) or a control condition (game play as a UN soldier/recall of acts that do not induce guilt). Results of the study indicate several important findings. First, the current results replicate previous research indicating that immoral virtual behaviors are capable of eliciting guilt. Second, and more importantly, the guilt elicited by game play led to intuition-specific increases in the salience of violated moral foundations. These findings indicate that committing ''immoral'' virtual behaviors in a video game can lead to increased moral sensitivity of the player. The potential prosocial benefits of these findings are discussed.
Reputation systems based on buyer feedback play an important role in today's online markets. In this article, we provide a rigorous methodology to establish a relationship between a seller's feedback history and risk of default. We validate this method against eBay's reputation system, using a dataset of terminated users (Not-A-Registered-User or NARU) and the feedback left for them by buyers. By treating feedback rating data as a function of time, we characterize the tendency of change in seller feedback ratings in order to predict the behavior of a seller. We find that NARU sellers have significantly more negative feedback in their final weeks. Applying functional principal component analysis and classification tree methods, we find that when projecting the feedback data to an appropriate space, NARU and non-NARU sellers can be distinguished at better than 92% accuracy. We use this to provide a quantitative mechanism for evaluating the risk of trading with a seller who has less than perfect feedback, and offer advice on how much a buyer should offer to pay, given an asking price on a commodity item and a seller's feedback history.
Gradient-domain rendering can highly improve the convergence of light transport simulation using the smoothness in image space. These methods generate image gradients and solve an image reconstruction problem with rendered image and the gradient images. Recently, a previous work proposed a gradient-domain volumetric photon density estimation for homogeneous participating media. However, the image reconstruction relies on traditional L1 reconstruction, which leads to obvious artifacts when only a few rendering passes are performed. Deep learning based reconstruction methods have been exploited for surface rendering, but they are not suitable for volume density estimation. In this paper, we propose an unsupervised neural network for image reconstruction of gradient-domain volumetric photon density estimation, more specifically for volumetric photon mapping, using a variant of GradNet with an encoded shift connection and a separated auxiliary feature branch, which includes volume based auxiliary features such as transmittance and photon density. Our network smooths the images on global scale and preserves the high frequency details on a small scale. We demonstrate that our network produces a higher quality result, compared to previous work. Although we only considered volumetric photon mapping, it's straightforward to extend our method for other forms, like beam radiance estimation. CCS Concepts • Computing methodologies → Neural network; Ray tracing; † Joint first author.
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