Learning to Diversify Neural Text Generation via Degenerative Model

Neural language models often fail to generate diverse and informative texts, limiting their applicability in real-world problems. While previous approaches have proposed to address these issues by identifying and penalizing undesirable behaviors (e.g., repetition, overuse of frequent words) from language models, we propose an alternative approach based on an observation: models primarily learn attributes within examples that are likely to cause degeneration problems. Based on this observation, we propose a new approach to prevent degeneration problems by training two models. Specifically, we first train a model that is designed to amplify undesirable patterns. We then enhance the diversity of the second model by focusing on patterns that the first model fails to learn. Extensive experiments on two tasks, namely language modeling and dialogue generation, demonstrate the effectiveness of our approach.Comment: IJCNLP-AACL2023 Findings, 10 page

Estimation of the Available Rooftop Area for Installing the Rooftop Solar Photovoltaic (PV) System by Analyzing the Building Shadow Using Hillshade Analysis

AbstractFor continuous promotion of the solar PV system in buildings, it is crucial to analyze the rooftop solar PV potential. However, the rooftop solar PV potential in urban areas highly varies depending on the available rooftop area due to the building shadow. In order to estimate the available rooftop area accurately by considering the building shadow, this study proposed an estimation method of the available rooftop area for installing the rooftop solar PV system by analyzing the building shadow using Hillshade Analysis. A case study of Gangnam district in Seoul, South Korea was shown by applying the proposed estimation method

F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax

Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive. We argue that the sub-optimal text generation is mainly attributable to the imbalanced token distribution, which particularly misdirects the learning model when trained with the maximum-likelihood objective. As a simple yet effective remedy, we propose two novel methods, F^2-Softmax and MefMax, for a balanced training even with the skewed frequency distribution. MefMax assigns tokens uniquely to frequency classes, trying to group tokens with similar frequencies and equalize frequency mass between the classes. F^2-Softmax then decomposes a probability distribution of the target token into a product of two conditional probabilities of (i) frequency class, and (ii) token from the target frequency class. Models learn more uniform probability distributions because they are confined to subsets of vocabularies. Significant performance gains on seven relevant metrics suggest the supremacy of our approach in improving not only the diversity but also the quality of generated texts.Comment: EMNLP 202

UniPrimer: A Web-Based Primer Design Tool for Comparative Analyses of Primate Genomes

Whole genome sequences of various primates have been released due to advanced DNA-sequencing technology. A combination of computational data mining and the polymerase chain reaction (PCR) assay to validate the data is an excellent method for conducting comparative genomics. Thus, designing primers for PCR is an essential procedure for a comparative analysis of primate genomes. Here, we developed and introduced UniPrimer for use in those studies. UniPrimer is a web-based tool that designs PCR- and DNA-sequencing primers. It compares the sequences from six different primates (human, chimpanzee, gorilla, orangutan, gibbon, and rhesus macaque) and designs primers on the conserved region across species. UniPrimer is linked to RepeatMasker, Primer3Plus, and OligoCalc softwares to produce primers with high accuracy and UCSC In-Silico PCR to confirm whether the designed primers work. To test the performance of UniPrimer, we designed primers on sample sequences using UniPrimer and manually designed primers for the same sequences. The comparison of the two processes showed that UniPrimer was more effective than manual work in terms of saving time and reducing errors

Development of the monthly average daily solar radiation map using A-CBR, FEM, and kriging method

Photovoltaic (PV) system could be implemented to mitigate global warming and lack of energy. To maximize its effectiveness, the monthly average daily solar radiation (MADSR) should be accurately estimated, and then an accurate MADSR map could be developed for final decision-makers. However, there is a limitation in improving the accuracy of the MADSR map due to the lack of weather stations. This is because it is too expensive to measure the actual MADSR data using the remote sensors in all the sites where the PV system would be installed. Thus, this study aimed to develop the MADSR map with improved estimation accuracy using the advanced case-based reasoning (A-CBR), finite element method (FEM), and kriging method. This study was conducted in four steps: (i) data collection; (ii) estimation of the MADSR data in the 54 unmeasured locations using the A-CBR model; (iii) estimation of the MADSR data in the 89 unmeasured locations using the FEM model; and (iv) development of the MADSR map using the kriging method. Compared to the previous MADSR map, the proposed MADSR map was determined to be improved in terms of its estimation accuracy and classification level. First published online: 03 May 201

Measuring the impact of apnea and obesity on circadian activity patterns using functional linear modeling of actigraphy data

<p>Abstract</p> <p>Background</p> <p>Actigraphy provides a way to objectively measure activity in human subjects. This paper describes a novel family of statistical methods that can be used to analyze this data in a more comprehensive way.</p> <p>Methods</p> <p>A statistical method for testing differences in activity patterns measured by actigraphy across subgroups using functional data analysis is described. For illustration this method is used to statistically assess the impact of apnea-hypopnea index (apnea) and body mass index (BMI) on circadian activity patterns measured using actigraphy in 395 participants from 18 to 80 years old, referred to the Washington University Sleep Medicine Center for general sleep medicine care. Mathematical descriptions of the methods and results from their application to real data are presented.</p> <p>Results</p> <p>Activity patterns were recorded by an Actical device (Philips Respironics Inc.) every minute for at least seven days. Functional linear modeling was used to detect the association between circadian activity patterns and apnea and BMI. Results indicate that participants in high apnea group have statistically lower activity during the day, and that BMI in our study population does not significantly impact circadian patterns.</p> <p>Conclusions</p> <p>Compared with analysis using summary measures (e.g., average activity over 24 hours, total sleep time), Functional Data Analysis (FDA) is a novel statistical framework that more efficiently analyzes information from actigraphy data. FDA has the potential to reposition the focus of actigraphy data from general sleep assessment to rigorous analyses of circadian activity rhythms.</p

Regulation of the Catabolic Cascade in Osteoarthritis by the Zinc-ZIP8-MTF1 Axis

SummaryOsteoarthritis (OA), primarily characterized by cartilage degeneration, is caused by an imbalance between anabolic and catabolic factors. Here, we investigated the role of zinc (Zn2+) homeostasis, Zn2+ transporters, and Zn2+-dependent transcription factors in OA pathogenesis. Among Zn2+ transporters, the Zn2+ importer ZIP8 was specifically upregulated in OA cartilage of humans and mice, resulting in increased levels of intracellular Zn2+ in chondrocytes. ZIP8-mediated Zn2+ influx upregulated the expression of matrix-degrading enzymes (MMP3, MMP9, MMP12, MMP13, and ADAMTS5) in chondrocytes. Ectopic expression of ZIP8 in mouse cartilage tissue caused OA cartilage destruction, whereas Zip8 knockout suppressed surgically induced OA pathogenesis, with concomitant modulation of Zn2+ influx and matrix-degrading enzymes. Furthermore, MTF1 was identified as an essential transcription factor in mediating Zn2+/ZIP8-induced catabolic factor expression, and genetic modulation of Mtf1 in mice altered OA pathogenesis. We propose that the zinc-ZIP8-MTF1 axis is an essential catabolic regulator of OA pathogenesis

DNA microarrays on a dendron-modified surface improve significantly the detection of single nucleotide variations in the p53 gene

Selectivity and sensitivity in the detection of single nucleotide polymorphisms (SNPs) are among most important attributes to determine the performance of DNA microarrays. We previously reported the generation of a novel mesospaced surface prepared by applying dendron molecules on the solid surface. DNA microarrays that were fabricated on the dendron-modified surface exhibited outstanding performance for the detection of single nucleotide variation in the synthetic oligonucleotide DNA. DNA microarrays on the dendron-modified surface were subjected to the detection of single nucleotide variations in the exons 5–8 of the p53 gene in genomic DNAs from cancer cell lines. DNA microarrays on the dendron-modified surface clearly discriminated single nucleotide variations in hotspot codons with high selectivity and sensitivity. The ratio between the fluorescence intensity of perfectly matched duplexes and that of single nucleotide mismatched duplexes was >5–100 without sacrificing signal intensity. Our results showed that the outstanding performance of DNA microarrays fabricated on the dendron-modified surface is strongly related to novel properties of the dendron molecule, which has the conical structure allowing mesospacing between the capture probes. Our microarrays on the dendron-modified surface can reduce the steric hindrance not only between the solid surface and target DNA, but also among immobilized capture probes enabling the hybridization process on the surface to be very effective. Our DNA microarrays on the dendron-modified surface could be applied to various analyses that require accurate detection of SNPs