RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

This paper presents a new data augmentation algorithm for natural understanding tasks, called RPN:Random Position Noise algorithm.Due to the relative paucity of current text augmentation methods. Few of the extant methods apply to natural language understanding tasks for all sentence-level tasks.RPN applies the traditional augmentation on the original text to the word vector level. The RPN algorithm makes a substitution in one or several dimensions of some word vectors. As a result, the RPN can introduce a certain degree of perturbation to the sample and can adjust the range of perturbation on different tasks. The augmented samples are then used to give the model training.This makes the model more robust. In subsequent experiments, we found that adding RPN to the training or fine-tuning model resulted in a stable boost on all 8 natural language processing tasks, including TweetEval, CoLA, and SST-2 datasets, and more significant improvements than other data augmentation algorithms.The RPN algorithm applies to all sentence-level tasks for language understanding and is used in any deep learning model with a word embedding layer.Comment: 10 pages, 4 figure

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page

CLEC16A variants conferred a decreased risk to allergic rhinitis in the Chinese population

Background: Allergic rhinitis (AR) is a chronic respiratory disease. Hereditary factors played a key role in the pathogenesis of the AR. This study investigated the association between CLEC16A variants and AR risk in the Chinese population.Methods: We applied Agena MassARRAY technology platform to genotype five single nucleotide polymorphisms (SNPs) located in CLEC16A in 1004 controls and 995 cases. The association between CLEC16A SNPs (rs2286973, rs887864, rs12935657, rs11645657 and rs36045143) and AR risk were calculated by logistic regression analysis, with odds ratio (OR) and 95% confidence interval (CI). False-positive report probability (FPRP) was also used to assess the significant results to reduce false positives. Multifactor dimensionality reduction (MDR) was completed to assess the interaction between CLEC16A variants to predict AR risk.Results: Totally, CLEC16A (rs887864, rs12935657, rs2286973, rs11645657 and rs36045143) were significantly associated with AR risk. Therein, rs2286973, rs11645657 and rs36045143 were related to a decreased risk of AR in the people ≤ 43 years old, females and the people with BMI≤24, respectively. And rs887864 and rs12935657 were also associated with a decreased susceptibility of AR in the people >43 years old. Meanwhile, in the results of region stratification, rs887864 conferred a reduced risk to AR in the people from loess hilly area.Conclusion:CLEC16A variants conferred a decreased risk to AR in the Chinese population

Soft holographic interference lithography microlens for enhanced organic light emitting diode light extraction

Very uniform 2 μm-pitch square microlens arrays (μLAs), embossed on the blank glass side of an indium-tin-oxide (ITO)-coated 1.1 mm-thick glass, are used to enhance light extraction from organic light-emitting diodes (OLEDs) by ~100%, significantly higher than enhancements reported previously. The array design and size relative to the OLED pixel size appear to be responsible for this enhancement. The arrays are fabricated by very economical soft lithography imprinting of a polydimethylsiloxane (PDMS) mold (itself obtained from a Ni master stamp that is generated from holographic interference lithography of a photoresist) on a UV-curable polyurethane drop placed on the glass. Green and blue OLEDs are then fabricated on the ITO to complete the device. When the μLA is ~15 × 15 mm2, i.e., much larger than the ~3 × 3 mm2 OLED pixel, the electroluminescence (EL) in the forward direction is enhanced by ~100%. Similarly, a 19 × 25 mm2μLA enhances the EL extracted from a 3 × 3 array of 2 × 2 mm2 OLED pixels by 96%. Simulations that include the effects of absorption in the organic and ITO layers are in accordance with the experimental results and indicate that a thinner 0.7 mm thick glass would yield a ~140% enhancement

Case report: Multiple arterial stenoses induced by autosomal-recessive hypophosphatemic rickets type 2 associated with mutation of ENPP1: a case study

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1)-related multiple arterial stenoses is a rare clinical syndrome in which global arterial calcification begins in infancy, with a high probability of early mortality, and hypophosphatemic rickets develops later in childhood. The vascular status of an ENPP1-mutated patient when they enter the rickets phase has not been thoroughly explored. In this study, we presented a case of an adolescent with an ENPP1 mutation who complained of uncontrolled hypertension. Systematic radiography showed renal, carotid, cranial, and aortic stenoses as well as random calcification foci on arterial walls. The patient was incorrectly diagnosed with Takayasu’s arteritis, and cortisol therapy had little effect on reducing the vascular stenosis. As a result, phosphate replacement, calcitriol substitution, and antihypertensive medication were prescribed, and the patient was discharged for further examination. This research presented the vascular alterations of an ENPP1-mutanted patient, and while there is less calcification, intimal thickening may be the primary cause of arterial stenosis

KARR-seq reveals cellular higher-order RNA structures and RNA–RNA interactions

RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA–RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA–RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA–RNA interactions between the viruses and the host RNAs that potentially regulate viral replication