The Role of Customer Attitudes in Building the Reputation of a Company Sponsoring Sport Events

This study examines the relationship between sponsorship-fit and customer’s attitude toward the sponsorship, and explains the effect of customer’s attitude on the relationship between sponsorship-fit and firm reputation. Specifically, the effect of customer’s attitude is analyzed as mediator in the relationship between sponsorship-fit and customer-based firm reputation. Regression analysis and Partial Least Square Structural Equation Modeling (PLSSEM) are employed to test the research hypotheses. Empirical findings show the importance of sponsorship-fit affects change in customer attitude customer’s attitude mediates the relationship between sponsorship fit and firm reputation

Implicit Kernel Attention

\textit{Attention} computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transformers and graph attention networks (GAT) are widely utilized for sequential data and graph-structured data. This paper suggests a new interpretation and generalized structure of the attention in Transformer and GAT. For the attention in Transformer and GAT, we derive that the attention is a product of two parts: 1) the RBF kernel to measure the similarity of two instances and 2) the exponential of $L^{2}$ norm to compute the importance of individual instances. From this decomposition, we generalize the attention in three ways. First, we propose implicit kernel attention with an implicit kernel function, instead of manual kernel selection. Second, we generalize $L^{2}$ norm as the $L^{p}$ norm. Third, we extend our attention to structured multi-head attention. Our generalized attention shows better performance on classification, translation, and regression tasks

Performance Evaluation of Diagnostic X-Ray Equipment Regarding the Hospital Size in the Republic of Korea

Introduction: The Republic of Korea has developed a national standard based on which diagnostic X-ray equipment must be tested every 3 years. Accordingly, the performance of X-ray equipment used in all hospitals is evaluated by national certification bodies in compliance with the safety management regulations for X-ray equipment. However, if the equipment is non-compliant, its use must be stopped until it satisfies the accepted standards. Material and Methods: In compliance with the safety management regulations for diagnostic X-ray equipment, hospitals in this study were divided into two groups, namely the general hospital group and the clinic group with diagnostic X-ray equipment. The samples in this study were composed of 11 and 18 machines selected randomly from general hospitals and clinics, respectively, which satisfied the acceptance standards since last year in both groups. The evaluation of diagnostic X-ray machines was based on the results obtained from X-ray tube voltage, tube current, exposure time accuracy, and the X-ray dose reproducibility. Results: The X-ray machines of the general hospital group followed all national standards. However, those of the clinic group failed to satisfy the requirements of tube voltage, tube current, exposure time accuracy, and X-ray dose reproducibility. Conclusion: Clinics require their own quality control to reduce unnecessary medical radiation exposure due to the poor X-ray equipment performance. Moreover, it is suggested that the test period of the safety management regulations on diagnostic X-ray equipment need to be shorter than three years

Generalized Gumbel-Softmax Gradient Estimator for Various Discrete Random Variables

Estimating the gradients of stochastic nodes is one of the crucial research questions in the deep generative modeling community, which enables the gradient descent optimization on neural network parameters. This estimation problem becomes further complex when we regard the stochastic nodes to be discrete because pathwise derivative techniques cannot be applied. Hence, the stochastic gradient estimation of discrete distributions requires either a score function method or continuous relaxation of the discrete random variables. This paper proposes a general version of the Gumbel-Softmax estimator with continuous relaxation, and this estimator is able to relax the discreteness of probability distributions including more diverse types, other than categorical and Bernoulli. In detail, we utilize the truncation of discrete random variables and the Gumbel-Softmax trick with a linear transformation for the relaxed reparameterization. The proposed approach enables the relaxed discrete random variable to be reparameterized and to backpropagated through a large scale stochastic computational graph. Our experiments consist of (1) synthetic data analyses, which show the efficacy of our methods; and (2) applications on VAE and topic model, which demonstrate the value of the proposed estimation in practices

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models. The approach introduces a discriminator that gives explicit supervision to a denoising sample path whether it is realistic or not. Unlike GANs, our approach does not require joint training of score and discriminator networks. Instead, we train the discriminator after score training, making discriminator training stable and fast to converge. In sample generation, we add an auxiliary term to the pre-trained score to deceive the discriminator. This term corrects the model score to the data score at the optimal discriminator, which implies that the discriminator helps better score estimation in a complementary way. Using our algorithm, we achive state-of-the-art results on ImageNet 256x256 with FID 1.83 and recall 0.64, similar to the validation data's FID (1.68) and recall (0.66). We release the code at https://github.com/alsdudrla10/DG.Comment: International Conference on Machine Learning (ICML23