Whom Do You Want to Be Friends With: An Extroverted or an Introverted Avatar? Impacts of the Uncanny Valley Effect and Conversational Cues

With the rapid growth of social virtual reality platforms, an increasing number of people will be interacting with others as avatars in virtual environments. Therefore, it is essential to develop a better understanding of the factors that could impact initial personality assessments and how they affect the willingness of people to befriend one another. Thin-slice judgment constitutes a quick judgment of a personality based on an avatar, and it could be impacted by the avatar’s appearance, particularly if the avatar elicits an uncanny valley effect that brings negative emotions such as eerieness. However, personality judgments and friendship decisions could also be influenced by social cues, such as conversational style. This experimental study investigated how these factors impact willingness to make friends with others in a virtual world. Drawing upon the uncanny valley effect and thin-slice judgment, this study examined how different levels of realism and conversational cues influence trustworthiness, likeability, and the willingness to be a friend. Furthermore, the current study tried to shed light on the interaction effects of realism and conversational cues to the dependent variables. In other words, this study investigated how this eventually influences one’s willingness to be a friend under the thin-slice judgment when personality judgments result from the negative feeling (i.e., eeriness) of the uncanny valley effect and social cues are conflicted. To this end, a 2 (realism: cartoonish vs. hyper-realistic) x 2 (conversational cues: extroverted vs. introverted) between-subjects online experiment was conducted. The results showed that trustworthiness and likeability significantly impacted the willingness to be a friend. Furthermore, realism and conversational cues marginally affected the willingness to be a friend. Keywords: uncanny valley effect, thin-slice judgment, avatar, personality judgment, willingness to be a frien

How do People Process and Share Fake News on Social Media?: In the context of Dual-Process of Credibility with Partisanship, Cognitive Appraisal to Threat, and Corrective Action

The objective of this study is to examine how the information processing of news users happens on social media in the context of spreading fake news. This study is intended to shed light on how fake news spreads on social media with the effects of two moderators (i.e., partisanship and source credibility) from political attitude consistency to message credibility and the effect of mediation (i.e., cognitive appraisal to threat) from message credibility to intent to share fake news on social media and corrective action. As a theoretical lens, dual-process theories were adopted in this paper. For this, a 2 (news topic: Immigration vs. Gun control) X 2 (news topic stance: Positive vs. Negative) X 2 (source: major (i.e., Associated Press) vs. minor (i.e., blog news) between-subject online experiment with 507 participants was conducted for both immigration and gun control topics. As a result, in the moderation effects, although partisanship was significant for both topic immigration and gun control news, source credibility was significant only for immigration news. Plus, the mediation effect of the cognitive appraisal to threats was significant between message credibility and the intent to share fake news on social media for both news topics. Lastly, even though the relations between message credibility and corrective action had to be negatively associated, they were positively correlated

U.S Audiences' Perceptions of Covid-19 and Conservative News Frames

During the early stages of the COVID-19 pandemic, U.S. conservative news downplayed the threat of the virus. Perceived risks of COVID-19 are an important factor in influencing citizens' willingness to comply with risk prevention measures. An online survey (N=269) of U.S. residents was conducted March 30 - April 1, 2020. We found that those who used partisan conservative news sources as their primary source of information about the virus were significantly less likely to view it as a threat, compared to those who cited Far Left, Center Left, and Center Right news sources. Politically conservative Far Right news audiences reported significantly lower estimates of their own COVID-19 risk, as well as that of their age group peers, the average person in the U.S., and the average senior citizen in the U.S

Noncommunicating Isolated Enteric Duplication Cyst in the Abdomen in Children: Report of One Case and Review of the Literature

Noncommunicating isolated enteric duplications in the abdomen are an extremely rare variant of enteric duplications with their own blood supply. We report a case of a noncommunicating isolated ileal duplication in a 10-month-old boy. He was admitted because of severe abdominal distension and developed irritability abruptly. Abdominal ultrasound and computed tomography scan revealed a closed loop of small bowel that was dilated severely. A large tubular cyst hanging on the narrow vascular pedicle arising from the base of the terminal ileum mesentery was found with torsion of the pedicle in the right upper quadrant of the abdomen. Laparoscopic excision was performed successfully. Here, we will also review the previously reported cases to raise awareness of noncommunicating isolated enteric duplications in the literature.Keywords: Abdomen, Children, Duplication, Isolated, Noncommunicatin

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization

Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all. As PTQ schemes based on reconstructing each layer or block output turn out to be effective to enhance quantized model performance, recent works have developed algorithms to devise and learn a new weight-rounding scheme so as to better reconstruct each layer or block output. In this work, we propose a simple yet effective new weight-rounding mechanism for PTQ, coined FlexRound, based on element-wise division instead of typical element-wise addition such that FlexRound enables jointly learning a common quantization grid size as well as a different scale for each pre-trained weight. Thanks to the reciprocal rule of derivatives induced by element-wise division, FlexRound is inherently able to exploit pre-trained weights when updating their corresponding scales, and thus, flexibly quantize pre-trained weights depending on their magnitudes. We empirically validate the efficacy of FlexRound on a wide range of models and tasks. To the best of our knowledge, our work is the first to carry out comprehensive experiments on not only image classification and natural language understanding but also natural language generation, assuming a per-tensor uniform PTQ setting. Moreover, we demonstrate, for the first time, that large language models can be efficiently quantized, with only a negligible impact on performance compared to half-precision baselines, achieved by reconstructing the output in a block-by-block manner.Comment: Accepted to ICML 202

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

Rotting infinitely many-armed bandits

We consider the infinitely many-armed bandit problem with rotting rewards, where the mean reward of an arm decreases at each pull of the arm according to an arbitrary trend with maximum rotting rate ϱ=o(1). We show that this learning problem has an Ω(max{ϱ1/3T,T−−√}) worst-case regret lower bound where T is the time horizon. We show that a matching upper bound O~(max{ϱ1/3T,T−−√}), up to a poly-logarithmic factor, can be achieved by an algorithm that uses a UCB index for each arm and a threshold value to decide whether to continue pulling an arm or remove the arm from further consideration, when the algorithm knows the value of the maximum rotting rate ϱ. We also show that an O~(max{ϱ1/3T,T3/4}) regret upper bound can be achieved by an algorithm that does not know the value of ϱ, by using an adaptive UCB index along with an adaptive threshold value