Selective Token Generation for Few-shot Natural Language Generation

Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among them, additive learning that incorporates a task-specific adapter on top of the fixed large-scale PLM has been popularly used in the few-shot setting. However, this added adapter is still easy to disregard the knowledge of the PLM especially for few-shot natural language generation (NLG) since an entire sequence is usually generated by only the newly trained adapter. Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) that selectively outputs language tokens between the task-general PLM and the task-specific adapter during both training and inference. This output token selection over the two generators allows the adapter to take into account solely the task-relevant parts in sequence generation, and therefore makes it more robust to overfitting as well as more stable in RL training. In addition, to obtain the complementary adapter from the PLM for each few-shot task, we exploit a separate selecting module that is also simultaneously trained using RL. Experimental results on various few-shot NLG tasks including question answering, data-to-text generation and text summarization demonstrate that the proposed selective token generation significantly outperforms the previous additive learning algorithms based on the PLMs.Comment: COLING 202

Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset) and Video Question and Answering (TVQA dataset). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods.Comment: 8 pages, 3 figures, Association for the Advancement of Artificial Intelligence (AAAI2020). arXiv admin note: substantial text overlap with arXiv:1907.0170

Image-to-Image Retrieval by Learning Similarity between Scene Graphs

As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks. In our approach, graph neural networks are trained to predict the proxy image relevance measure, computed from human-annotated captions using a pre-trained sentence similarity model. We collect and publish the dataset for image relevance measured by human annotators to evaluate retrieval algorithms. The collected dataset shows that our method agrees well with the human perception of image similarity than other competitive baselines.Comment: Accepted to AAAI 202

Serum alanine aminotransferase levels are closely associated with metabolic disturbances in apparently healthy young adolescents independent of obesity

Purpose Liver metabolism plays a pivotal role in the development of metabolic disorders. We aimed to investigate the clinical and laboratory risk factors associated with alanine aminotransferase (ALT) levels in young adolescents from an urban population in Korea. Methods A population of 120 apparently healthy adolescents aged 12–13 years was included in the cross-sectional design study; 58 were overweight or obese and 62 were of normal weight. We estimated anthropometric and laboratory measurements, including waist-to-height ratio, blood pressure, insulin sensitivity, aspartate aminotransferases (AST), ALT, and lipid profiles. Results The mean ages of the overweight or obese and normal weight participants were 12.9±0.3 and 13.0±0.3 years, respectively. Height, weight, body mass index, waist circumference, waist-to-height ratio, systolic and diastolic blood pressure, AST, ALT, total cholesterol, low-density lipoprotein-cholesterol, triglyceride, insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR) score were significantly higher and the high-density lipoprotein-cholesterol and quantitative insulin-sensitivity check index were significantly lower in the overweight/obese participants in comparison to the normal-weight participants (all P<0.05). In multivariate linear regression analysis, waist-to-height ratio, systolic blood pressure, and HOMA-IR score were independently and positively associated with serum ALT levels. Conclusion Screening for ALT levels in adolescents may help to differentiate those at risk of metabolic abnormalities and thus prevent disease progression at an early age

Comparing the neurologic outcomes of patients with out-of-hospital cardiac arrest according to prehospital advanced airway management method and transport time interval

Objective The incidences of prehospital advanced airway management by emergency medical technicians in South Korea are increasing; however, whether this procedure improves the survival outcomes of patients experiencing out-of-hospital cardiac arrest remains unclear. The present study aimed to investigate the association between prehospital advanced airway management and neurologic outcomes according to a transport time interval (TTI) using the Korean Cardiac Arrest Research Consortium database. Methods We retrospectively analyzed the favorable database entries that were prospectively collected between October 2015 and December 2016. Patients aged 18 years or older who experienced cardiac arrest that was presumed to be of a medical etiology and that occurred prior to the arrival of emergency medical service personnel were included. The exposure variable was the type of prehospital airway management provided by emergency medical technicians. The primary endpoint was a favorable neurologic outcome. Results Of 1,871 patients who experienced out-of-hospital cardiac arrest, 785 (42.0%), 121 (6.5%), and 965 (51.6%) were managed with bag-valve-mask ventilation, endotracheal intubation (ETI), and supraglottic airway (SGA) devices, respectively. SGAs and ETI provided no advantage in terms of favorable neurologic outcome in patients with TTIs ≥12 minutes (odds ratio [OR], 1.37; confidence interval [CI], 0.65–2.87 for SGAs; OR, 1.31; CI, 0.30–5.81 for ETI) or in patients with TTI <12 minutes (OR, 0.57; CI, 0.31–1.07 for SGAs; OR, 0.63; CI, 0.12–3.26 for ETI). Conclusion Neither the prehospital use of SGA nor administration of ETI was associated with superior neurologic outcomes compared with bag-valve-mask ventilation

The effectiveness of Paxlovid treatment in long-term care facilities in South Korea during the outbreak of the Omicron variant of SARS-CoV-2

Objectives On November 5, 2021, Pfizer Inc. announced Paxlovid (nirmatrelvir+ritonavir) as a treatment method that could reduce the risk of hospitalization or death for patients with confirmed coronavirus disease 2019 (COVID-19). Methods From February 6, 2022 to April 2, 2022, the incidence of COVID-19 and the effects of treatment with Paxlovid were analyzed in 2,241 patients and workers at 5 long-term care facilities during the outbreak of the Omicron variant of severe acute respiratory syndrome coronavirus 2 in South Korea. Results The rate of severe illness or death in the group given Paxlovid was 51% lower than that of the non-Paxlovid group (adjusted risk ratio [aRR], 0.49; 95% confidence interval [CI], 0.24−0.98). Compared to unvaccinated patients, patients who had completed 3 doses of the vaccine had a 71% reduced rate of severe illness or death (aRR, 0.29; 95% CI, 0.13−0.64) and a 65% reduced death rate (aRR, 0.35; 95% CI, 0.15−0.79). Conclusion Patients given Paxlovid showed a lower rate of severe illness or death and a lower fatality rate than those who did not receive Paxlovid. Patients who received 3 doses of the vaccine had a lower rate of severe illness or death and a lower fatality rate than the unvaccinated group

High performance carbon nanotubes thin film transistors by selective ferric chloride doping

Single wall carbon nanotubes (SWNT) have been a significant research topic as active layers for thin film transistors (TFTs) due to their high charge carrier mobility beyond that of crystalline silicon. In this study, we report an effective approach to achieve a very high field-effect mobility and on/off ratio for solution processed semiconducting SWNT TFTs, by selective doping through contact with a thin ferric chloride (FeCl3) dopant layer. The semiconducting layer is formed by a double spin coating of the highly purified (&gt;99%) high pressure carbon mono oxide (HiPCO) SWNT sorted by wrapping of poly (3-dodecylthiophene-2,5-diyl) (P3DDT). In order to achieve effective hole injection from the top Au source electrode without increasing the off-state drain current, less purified (98-99%) SWNTs produced by the plasma discharge process sorted by wrapping of poly (9,9-di-n-dodecylfluorene) (PFDD) are formed on the top of HiPCO film. Significantly improved TFT performance is achieved by the insertion of a few nanometers of a FeCl3 dopant layer at the semiconductor-contact interface. A significant high hole field-effect of 48.35 +/- 3.11 cm(2)V(-1)s(-1) (bare: 6.18 +/- 0.87 cm(2)V(-1)s(-1)) with a reasonable on/off current ratio of 10(5), and low off current of similar to 80 pA, are obtained by controlling the concentration of FeCl3 dopant (thickness = 1.5 nm) at the contact. Mobility is improved further at 2.5 nm thickness of the FeCl3 dopant layer resulting in a hole mobility of 177 +/- 13.2 cm(2) V(-1)s(-1), an on/off ratio of 7.4 x 10(3), and off state current of 1.2 x 10(-9) A