Empirical Rationalization of Prior Substantiation Doctrine: Federal Trade Commission v. Reebok & Sketchers

Companies frequently make efficacy claims in advertisements to introduce new products featuring innovative technology. When such claims are supported by information obtained from scientific research or expert testimonials, they are subject to the doctrine of prior substantiation. Under the doctrine, an advertisement claim based on seemingly credible authorities must be substantiated by a reasonable basis before it is released to the general public. Otherwise, the advertisement will be in violation of Section 5(a) of the Federal Trade Commission Act that prohibits unfair or deceptive acts affecting commerce. \u27 This study investigates the rationale of the legal rule in light of consumer behavior theories. While the doctrine has been normatively rationalized, it has not been empirically examined. Given the paucity of relevant research, this study will test consumer attitudes and cognitive reactions toward different types of advertisement messages, such as, one with establishment claims and the other without such cognitive contents. The study administered real advertising video clips used by Reebok and Sketchers, disputed in two settled cases where the Federal Trade Commission alleged that the defendants failed to satisfy the legal standard of the substantiation rule. The findings of this study support the rationale of the rule on the ground that the Reebok advertisement clip delivering expressive establishment claims about its product efficacy would likely have more of an immediate impact on consumers\u27 purchasing intention than Sketchers\u27 ad without such cognitive information. Implications and future research along with limitations are also discussed

Phenanthroline diimide as an organic electron-injecting material for organic light-emitting devices

We report a diimide-type organic electron-injecting material, bis-[1,10]phenanthrolin-5-yl-pyromellitic diimide (Bphen-PMDI), for organic light-emitting devices (OLEDs), which was synthesized from its monomers, pyromellitic dianhydride (PMDA) and 1,10-phenanthrolin-5-amine (PTA). The vacuum-purified Bphen-PMDI powder showed high glass transition (∼230°C) and thermal decomposition (∼400°C) temperatures, whereas neither melting point nor particular long-range crystal nanostructures were observed from its solid samples. The optical band gap energy and the ionization potential of the Bphen-PMDI film were 3.6 eV and 6.0 eV, respectively, leading to the lowest unoccupied molecular orbital (LUMO) energy of 2.4 eV. Inserting a 1 nm thick Bphen-PMDI layer between the emission layer and the cathode layer improved the device current density by 10-fold and the luminance by 6-fold, compared to the OLED without the Bphen-PMDI layer. The result suggests that an effective electron tunnel injection process occurs through the Bphen-PMDI layer. © The Royal Society of Chemistry 2012.1

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties.Comment: Accepted at IEEE International Conference on Industrial Informatics 2023(INDIN 2023

Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery

Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch. To address the limitations and more accurately reflect real-world scenarios, in this paper, we propose a novel unsupervised class incremental learning approach for discovering novel categories on unlabeled sets without prior knowledge. The proposed method fine-tunes the feature extractor and proxy anchors on labeled sets, then splits samples into old and novel categories and clusters on the unlabeled dataset. Furthermore, the proxy anchors-based exemplar generates representative category vectors to mitigate catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods on fine-grained datasets under real-world scenarios.Comment: Accepted to ICCV 202

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Alcohol intake and cardiovascular risk factors:A Mendelian randomisation study

Mendelian randomisation studies from Asia suggest detrimental influences of alcohol on cardiovascular risk factors, but such associations are observed mainly in men. The absence of associations of genetic variants (e.g. rs671 in ALDH2) with such risk factors in women – who drank little in these populations – provides evidence that the observations are not due to genetic pleiotropy. Here, we present a Mendelian randomisation study in a South Korean population (3,365 men and 3,787 women) that 1) provides robust evidence that alcohol consumption adversely affects several cardiovascular disease risk factors, including blood pressure, waist to hip ratio, fasting blood glucose and triglyceride levels. Alcohol also increases HDL cholesterol and lowers LDL cholesterol. Our study also 2) replicates sex differences in associations which suggests pleiotropy does not underlie the associations, 3) provides further evidence that association is not due to pleiotropy by showing null effects in male non-drinkers, and 4) illustrates a way to measure population-level association where alcohol intake is stratified by sex. In conclusion, population-level instrumental variable estimation (utilizing interaction of rs671 in ALDH2 and sex as an instrument) strengthens causal inference regarding the largely adverse influence of alcohol intake on cardiovascular health in an Asian population

Characteristics and Clinical Outcomes of Elderly Patients with Trauma Treated in a Local Trauma Center

Purpose This study aimed to investigate the characteristics of elderly patients who visited a non-regional trauma center to examine the effects of old age on the clinical outcomes of patients. Methods The medical charts of 159 patients with trauma who visited the National Health Insurance Service Ilsan Hospital between March 2020 and February 2022 were retrospectively analyzed. Results Of the 159 patients, 41 were assigned to the elderly patient group (EPG) and 118 were assigned to the non-elderly patient group (NEPG). The average age of patients in each group was 75.5 and 38.2 years in the EPG and the NEPG, respectively. Comparing the injury mechanism between the two groups, pedestrian traffic accidents (TA) were the most common (24.4%), followed by slipping (19.5%), motorcycle TA, and bicycle TA (14.6%) in EPG. In the NEPG, motorcycle TA (28.0%) was the most common, followed by car TA (27.1%), and fall injury (16.9%), with a significant difference between the two groups (p < 0.001). The significant differences between the two groups were the injury severity score (ISS; p = 0.004), severe trauma (p = 0.045), intensive care unit admission (p = 0.028), emergency operation (p = 0.034), and mortality (p = 0.013). The statistically significant risk factors for mortality were old age (p = 0.024) and chest injury (p = 0.013). Conclusion Patients in the EPG compared with the NEPG group showed different injury mechanisms. The EPG has a higher severity and mortality rate than the NEPG