Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation

Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. How-ever, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Ex-amples of these approaches include an ap-proach that uses a polarity dictionary that can-not handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word rep-resentation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks.

Natural Language Generation for Advertising: A Survey

Natural language generation methods have emerged as effective tools to help advertisers increase the number of online advertisements they produce. This survey entails a review of the research trends on this topic over the past decade, from template-based to extractive and abstractive approaches using neural networks. Additionally, key challenges and directions revealed through the survey, including metric optimization, faithfulness, diversity, multimodality, and the development of benchmark datasets, are discussed

Na+-leak channel, non-selective (NALCN) regulates myometrial excitability and facilitates successful parturition

Background/Aims: Uterine contractility is controlled by electrical signals generated by myometrial smooth muscle cells. Because aberrant electrical signaling may cause inefficient uterine contractions and poor reproductive outcomes, there is great interest in defining the ion channels that regulate uterine excitability. In human myometrium, the Na+ leak channel, non-selective (NALCN) contributes to a gadolinium-sensitive, Na+-dependent leak current. The aim of this study was to determine the role of NALCN in regulating uterine excitability and examine its involvement in parturition. Methods: Wildtype C57BL/6J mice underwent timed-mating and NALCN uterine expression was measured at several time points across pregnancy including pregnancy days 7, 10, 14, 18 and 19. Sharp electrode current clamp was used to measure uterine excitability at these same time points. To determine NALCN’s contribution to myometrial excitability and pregnancy outcomes, we created smooth-muscle-specific NALCN knockout mice by crossing NALCNfx/fx mice with myosin heavy chain Cre (MHCCreeGFP) mice. Parturition outcomes were assessed by observation via surveillance video recording cre control, flox control, smNALCN+/-, and smNALCN-/- mice. Myometrial excitability was compared between pregnancy day 19 flox controls and smNALCN-/- mice. Results: We found that in the mouse uterus, NALCN protein levels were high early in pregnancy, decreased in mid and late pregnancy, and then increased in labor and postpartum. Sharp electrode current clamp recordings of mouse longitudinal myometrial samples from pregnancy days 7, 10, 14, 18, and 19 revealed day-dependent increases in burst duration and interval and decreases in spike density. NALCN smooth muscle knockout mice had reduced myometrial excitability exemplified by shortened action potential bursts, and an increased rate of abnormal labor, including prolonged and dysfunctional labor. Conclusions: Together, our findings demonstrate that the Na+ conducting channel NALCN contributes to the myometrial action potential waveform and is important for successful labor outcomes

Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction. However, regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics, especially at tunnel portals with complex geological conditions. This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis, with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals. The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages. To improve the accuracy of the models, a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation. Finally, a regional-scale model and an engineering-scale model are built, providing a clear insight into geological phenomena and supporting numerical calculation. In addition, the proposed framework is applied to a case study, the Long-tou mountain tunnel project in Guangzhou, China, where the dominant rock type is granite. The results show that the data integration and modeling methods effectively improve model structure refinement. The improved model's calculation deviation is reduced by about 10% to 20% in the mechanical analysis. This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a \u27clock\u27 of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal \u27clock\u27 of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman\u27s), indicating that our model assigns a more advanced GA when an individual\u27s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs

Surface Plasmon Enhancement on Infrared Photodetection

AbstractInAsSb based infrared photodetector is an alternative to the existing HgCdTe, PbSnTe, and InSb counterparts, but its room temperature performance is still relatively poor. One of the ways to improve its performance is through surface plasmon, which provides near field confinement that leads to enhancement in light matter interaction. In this work, the role of each parameter of two dimensional metallic hole arrays in plasmonic enhancement is studied in details, such as the periodicity of hole array, hole diameter and metal film thickness. The plasmonic resonances and their corresponding electric field distributions are comprehensively studied in finite difference time domain simulation, which also would serve as a guide for designing surface plasmon enhanced InAsSb infrared detector with high quantum efficiency and signal-to-noise ratio

Microstructure and texture evolutions in FeCrAl cladding tube during pilger processing

The microstructure of FeCrAl cladding tubes depends on the fabricating process history. In this study, the microstructural characteristics of wrought FeCrAl alloys during industrial pilger processing into thin-walled tubes were investigated. The hot extruded tube showed ∼100 μm equiaxed grains with weak α∗-fiber in {h11}<1/h12> texture, while pilger rolling process change the microstructure to fragmented and elongated grains along the rolling direction. The pilgered textures could be predicted with the VPSC model. The inter-pass annealing at 800–850 \ub0C for 1 h results in recovery and recrystallization of the ferric matrix and restoration of ductility. The final finished tube shows fine recrystallized grains (∼11 μm) with dominant γ-fiber in three dimensions. Pilger rolling enhanced α-fiber while annealing reduced α-fiber and enhanced γ-fiber. Microstructural evolution in the Laves precipitates followed the sequence of faceted needle-like → spherical → faceted ellipsoidal. Thermomechanical processing resulted in cladding tubes with an area fraction of ∼5% and a number density of 5 7 10−11 m−2 in Laves precipitates, which is half that of the first-pilgered tube. Laves precipitates pin the grain boundaries to control the microstructure and prevent grain coarsening

Analysis of electrophysiological activation of the uterus during human labor contractions

This cohort study uses electromyometrial imaging to examine the underlying electrophysiological origins of human labor at the myometrium level