196 research outputs found
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 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 norm as the 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
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
This paper explores the challenges posed by aspect-based sentiment
classification (ABSC) within pretrained language models (PLMs), with a
particular focus on contextualization and hallucination issues. In order to
tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review
Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a
benchmark dataset that incorporates aspects and dual-tagged polarities to
distinguish between aspect-specific and aspect-agnostic sentiment
classification. The dataset consists of sentences annotated with specific
aspects, aspect polarity, aspect-agnostic polarity, and the intensity of
aspects. To address the issue of dual-tagged aspect polarities, we propose a
novel approach employing a Siamese Network. Our experimental findings highlight
the inherent difficulties in accurately predicting dual-polarities and
underscore the significance of contextualized sentiment analysis models. The
CARBD-Ko dataset serves as a valuable resource for future research endeavors in
aspect-level sentiment classification
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
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