Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends and patterns in the contributions to cumulative radiative forcing from different regions of the world

Significance The cumulative radiative forcing (CRF), an integral of radiative forcing over a given time, is used to calculate the contribution to the global mean net radiative forcing due to greenhouse gases and aerosols from various regions. For every region, the commitment to future radiative forcing from emissions already in the atmosphere is larger than that to date. For individual regions, the CRF is near zero during rapid industrialization and it increases when air-quality regulations come in and/or exponential growth ceases, a pattern repeated by North America, Europe, and China. The relative contributions from regions depends more on when the contributions are compared (e.g., now or 2100) and development scenarios than the metric used (i.e., CRF, ocean heat content, or temperature). Different regions of the world have had different historical patterns of emissions of carbon dioxide, other greenhouse gases, and aerosols as well as different land-use changes. One can estimate the net cumulative contribution by each region to the global mean radiative forcing due to past greenhouse gas emissions, aerosol precursors, and carbon dioxide from land-use changes. Several patterns stand out from such calculations. Some regions have had a common historical pattern in which the short-term offsets between the radiative forcings from carbon dioxide and sulfate aerosols temporarily led to near-zero radiative forcing during periods of exponential emissions growth with few emission controls. This happened for North America and Europe in the mid-20th century and China in the 1990s and 2000s. However, these same periods lead to a commitment to future radiative forcing from the carbon dioxide and other greenhouse gases that stay in the atmosphere long after the aerosols. For every region, this commitment to future radiative forcing (2018–2100) from emissions already in the atmosphere is larger than the cumulative radiative forcing to date (1900–2017). This comparison again highlights how the full radiative forcing from greenhouse gases is unmasked once the aerosol emissions are reduced to improve air quality. The relative contributions from various regions to global climate forcing depends more on the time the contributions are compared (e.g., now or 2100) and future development scenarios than on whether cumulative radiative forcing, ocean heat content, or temperature is used to compare regional contributions.
A study on integrating distinct classifiers with bidirectional LSTM for Slot Filling task

In spite of being investigated for decades, in Spoken Language Understanding, slot filling task perceived as sequential labeling in a specific domain is still challenging and attractive to many researchers. For this task, Recurrent Conditional Random Field (RCRF) is a popular model in order to learn latent representations of data which are then utilized as input to a classifier CRF. Our proposed model, in contrast, employed a variant of RNNs, called Long Short-Tem Memory Networks (LSTMs) which, more or less, tackle the downside of RNNs: vanishing gradients. Additionally, we also conducted experiments on the integration of bidirectional LSTM with distinct classifiers, e.g CRFs, SVMs; which then are trained simultaneously. The experimental results show that these combinations are beneficial on both dataset Airline Travel Information System (ATIS) and DARPA Communicator, compared with the state-of-the-art model.
A Review on Isolated and Non-isolated DC-DC Converter for PV Application

In today,s scenario. , the High step-up DC-DC converters have been widely used in the renewable energy systems. It has been attaining popularity due to their increasing practices and extensive application in photovoltaic, fuel cell, uninterrupted power supply and electric vehicles. A review is presented to demonstrate the various isolated and non-isolated DC-DC converter topologies, different isolated flyback topologies and recent trades. Its main features are high voltage conversion ratio, low cost, low voltage stress on the switches, simplicity, galvanic isolation and high efficiency performance are covered and classified in to several categories.
Improving Optical Braille Recognition in Pre-processing Stage

Braille documents are captured and processed and converted into natural text using Optical Braille Recognition (OBR) system. Braille data or information which is embossed on Braille plate is most commonly captured by mobile camera or scanner into Braille image is the input to OBR. Preprocessing is the first stage performed by OBR system. The Noise introduced while Image captured using camera, mobile or scanners involve irregular lightness, low quality image due to less pixel camera, impulse noise, diverse gray-level values, angled or slanted image captured as a result of human error, deformation or warp of image document, deprived or disabled dots, appearance of unwanted dots, irregular gap between adjacent dots that represent a character. Various image enhancement techniques are applied in preprocessing stage to abolished or reduce such impulse noises. The main mission of the paper is to understand and apply a proportional learning of diverse preprocessing techniques on the Braille image and identify the best method to improve quality of Braille image.
Association between Adherence to the Japanese Food Guide Spinning Top and Sleep Quality in College Students

This study aimed to elucidate the association between adherence to the Japanese Food Guide Spinning Top (Food Guide score) and sleep quality in Japanese college students. We conducted a cross-sectional study of 175 Japanese college students aged 19–22 years in the eastern part of Gunma Prefecture to examine the association between the Food Guide score and sleep quality. A self-administered diet history questionnaire and the Pittsburgh Sleep Quality Index were used to assess habitual dietary intake and sleep quality, respectively. In the fully adjusted model, the odds ratios for poor sleep quality in the middle and highest tertile categories of the Food Guide score were 0.50 (95% confidence interval, 0.18–1.37) and 0.30 (95% confidence interval, 0.11–0.84), respectively, compared with those in the lowest tertile category (p for trend = 0.033). A well-balanced diet may be associated with good sleep quality in Japanese college students.
Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks

As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
Eye Gaze Correction Using Generative Adversarial Networks

Eye gaze correction is an important topic in video teleconference and video chart in order to keep the eye contact. In this paper, we propose to use a generative adversarial networks for eye gaze correction. We use pairs of front facial image (idea camera setting) and real facial image (real camera setting) to training the network. By using the trained network, we can generate a gaze corrected facial image (front facial image) for any real facial image. Experiments demonstrated the effectiveness of our proposed method.
The Transcriptional Signature of a Runner’s High

Introduction Endorphins, endocannabinoids, monoamines, and neurotrophins have all been implicated in the euphoric response to endurance running, known as a runner’s high (RH). The epitranscriptional mechanisms regulating this effect have not been defined. Here, we investigate peripheral micro–ribonucleic acid (miRNA) changes unique to athletes experiencing postrun euphoria, yielding insights into gene networks that control an RH. Methods A cohort study involving 25 collegiate runners (48% females, age = 20 ± 1 yr) examined salivary RNA levels before and after a long-distance run. Participants were divided into RH and nonrunner’s high (NRH) groups based on surveys of four criteria (mood, lost sense of time, run quality, and euphoria). Physiological measures were also recorded (temperature, heart rate, blood pressure, pupillary dilatation, and salivary serotonin). Levels of miRNAs and their messenger RNA targets were compared across pre- and postrun samples from RH and NRH groups with two-way ANOVA. Representation of opioid, gamma-aminobutyic acid (GABA), endocannabinoid, neurotrophin, serotonergic, and dopaminergic pathways was assessed in DIANA miRPath. Pearson’s correlation analyses examined relationships between miRNAs and RH indices. Results RH participants (n = 13) demonstrated postrun mydriasis (P = 0.046) and hypothermia (P = 0.043) relative to NRH participants (n = 12) but had no difference in serotonin dynamics (P = 0.88). Six miRNAs (miR-194-5p, miR-4676-3p, miR-4254, miR-4425, miR-1273-3p, miR-6743-5p) exhibited significant effects (false discovery rate P value < 0.05) across pre- or postrun and RH/NRH groups. These miRNAs displayed target enrichment for opioid (P = 2.74E−06) and GABA (P = 0.00016) pathways. miR-1237-3p levels were related with lost sense of time (R = 0.40). Mitogen-activated protein kinase (MAPK11), an endocannabinoid target of miR-1273-3p, was nominally elevated in RH participants (false discovery rate P value = 0.11). Conclusions Unique dynamics in miRNA concentration occur in athletes with subjective/objective evidence of RH, targeting genes implicated endorphin, endocannabinoid, and GABAergic signaling.
A Comparative Analysis of Identifying Influential Users in Online Social Networks

It has been the one of the primary efforts of social studies for decades to find influential people in a society due to its numerous applications, such as spreading ideas and practices. In today's world, Online Social Networks (OSNs) support their users by analyzing the behaviors of their users and helping them to maintain/create social presence. Several techniques for analyzing social networks have been developed, to gauge quantitative properties or qualitative aspects. However, online social network analysis poses novel challenges both to computer and social scientists. Influential User is a unique web and social phenomenon affecting tastes and behaviors of their users and helping them to maintain/create friendships. Since the influence of good number of connections/friends needs to be evaluated for each user, it is difficult to infer precisely which among them is influential. Many models ranging from Associative Rule mining to analyzing a bunch of social network metrices have been proposed. In all of the proposed methods, the very first step is to quantify the amount of influence an individual exerts on another. There can be many different ways to calculate the power of user. We demonstrate how these models are able to find the influential users. Besides we do an analytical comparison between different methods. Eventually, we conclude that there is no single method that is better than others as the problem is hard, so the space for new algorithms is wide open.
Hyperspectral Image Classification Based on Convolutional Neural Network

The deep learning is of great interest recently. MIT Technology Review ranked deep learning as the top 10 technolog y breakthrough in 2013. The multi-layered structure of Deep Lear ning Network enables it to learn deeper features of data and impr ove the accuracy of data identification and classification. Hypers pectral image classification technology is the most important rese arch direction in hyperspectral remote sensing, and has been wid ely used in military and civil fields. It has become a trend to appl y deep learning to hyperspectral image classification. But deep Ie arning is data-driven and requires a lot of data, however, the hyp erspectral data we deal with is a small data set. In the classificatio n of small data sets, compared to the traditional classification met hods, classification of the deep learning is more effective. In this p aper, we study the convolution neural network which is excellent in the field of image processing, and compare the effect of hypers pectral image classification with the traditional Classification met hod
Path Planning with Obstacle Avoidance Based on Normalized R-Functions

Existing methods for path planning with obstacle avoidance need to check having the interference between a moving part and an obstacle at iteration and even to calculate their shortest distance in the case of given motion parameters. Besides, the tasks like collision-checking and minimum-distance calculating themselves are complicated and time-consuming. Rigorous mathematical analysis might be a practical way for dealing with the above-mentioned problems. An R-function is a real-valued function whose properties are fully determined by corresponding attributes of their parameters, which is usually applied to express a geometrical object. Thus, a signed distance function based on R-functions is created to represent whether two objects intervene and their level of intervention or separation. As the signed function is continuous and differentiable, the gradient information of the objective function guides a moving part to avoid its obstacles and to approach its target position rapidly. Therefore, a path planning approach with obstacle avoidance based on normalized R-functions is proposed in this paper. A discrete convex hull approach is adopted to solve the problem that R-function is inappropriate to represent a geometric object with some curves or surfaces, and pendent points and edges are generated in Boolean operations. Besides, a normalized approach ensures accuracy calculation of signed distance function. Experimental results have shown that the presented approach is a feasible way for path planning with obstacle avoidance.
Evaluation of a Thermal-Based Flow Meter for Assessment of Mobile Resting Metabolic Rate Measures

This work evaluates the use of a new flow meter to assess exhalation rate. A mobile indirect calorimeter (MIC) was designed and used to measure resting metabolic rate (RMR), which relies on the measure of O2 consumption rate (VO2) and CO2 production rate (VCO2). The device was produced from a commercially available and well-established indirect calorimeter and implemented with a new flow meter for the purpose of this study. VO2 and VCO2 were assessed by measuring exhalation rates using the new flow meter and O2 and CO2 concentrations in breath using the original colorimetric sensors of the indirect calorimeter. The new flow meter was based on a thermal flow meter (TFM) affixed to an orifice with a diameter of 6.8 mm used as a passage for exhaled breath from 16 subjects. The results were compared with a metabolic cart (Medical Graphics), which was connected in series to the modified device. We found that 69% of the results had more than a 10% difference between the modified MIC device and the reference instrument, suggesting that the sensitivity of the thermal flow meter changed over time, which precluded its use as a flow meter for breath flow rate measurement.
A New Formula for Vietnamese Text Readability Assessment

Text readability has an important role in text drafting and document selecting. Most studies on the readability of the text have been made long ago for English and some common languages. There are few researches about Vietnamese text readability and most of them are performed more than three decades ago on very small corpora. In this paper, we build a new and larger corpus and use it to create a newer formula to predict the difficulty of Vietnamese text. The experimental results show that the new formula can predict the readability of Vietnamese documents with over 80% accuracy.
1.1 THz U-Silicon-On-Glass (U-SOG) Waveguide: A Low-Loss Platform for THz High-Density Integrated Circuits

A U-shaped silicon-on-glass (U-SOG) dielectric waveguide, operating at THz frequencies, is proposed here for the first time. The modal analysis results for a single-mode U-SOG waveguide are also presented. The U-SOG waveguide is fabricated using micromachining system that allows for low-cost and high-precision manufacturing. Measurement results show that the U-SOG waveguide achieves an attenuation constant as low as 0.026 dB/λg (on average) over the frequency range of 0.9–1.08 THz. This measured attenuation is among the lowest reported for THz waveguides in the literature. The proposed U-SOG waveguide is a strong candidate for low-loss and low-cost THz high-density integrated systems.
High prevalence and adverse health effects of loneliness in community-dwelling adults across the lifespan: role of wisdom as a protective factor

ABSTRACT Objectives: This study of loneliness across adult lifespan examined its associations with sociodemographics, mental health (positive and negative psychological states and traits), subjective cognitive complaints, and physical functioning. Design: Analysis of cross-sectional data Participants: 340 community-dwelling adults in San Diego, California, mean age 62 (SD = 18) years, range 27–101 years, who participated in three community-based studies. Measurements: Loneliness measures included UCLA Loneliness Scale Version 3 (UCLA-3), 4-item Patient-Reported Outcomes Measurement Information System (PROMIS) Social Isolation Scale, and a single-item measure from the Center for Epidemiologic Studies Depression (CESD) scale. Other measures included the San Diego Wisdom Scale (SD-WISE) and Medical Outcomes Survey- Short form 36. Results: Seventy-six percent of subjects had moderate-high levels of loneliness on UCLA-3, using standardized cut-points. Loneliness was correlated with worse mental health and inversely with positive psychological states/traits. Even moderate severity of loneliness was associated with worse mental and physical functioning. Loneliness severity and age had a complex relationship, with increased loneliness in the late-20s, mid-50s, and late-80s. There were no sex differences in loneliness prevalence, severity, and age relationships. The best-fit multiple regression model accounted for 45% of the variance in UCLA-3 scores, and three factors emerged with small-medium effect sizes: wisdom, living alone and mental well-being. Conclusions: The alarmingly high prevalence of loneliness and its association with worse health-related measures underscore major challenges for society. The non-linear age-loneliness severity relationship deserves further study. The strong negative association of wisdom with loneliness highlights the potentially critical role of wisdom as a target for psychosocial/behavioral interventions to reduce loneliness. Building a wiser society may help us develop a more connected, less lonely, and happier society.
Three-Dimensional Joint Geometric-Physiologic Feature for Lip-Reading

Lip-reading has been successfully demonstrated that it can improve the performance of automatic speech recognition system especially in the presence of acoustic noise. However, the information about lip movement is still insufficient as the lip features are obtained from discrete three-dimensional points and planar images. The internal mechanisms of lip movement are not described and reflected. In this paper, we employed a novel deepening technique, namely densely connected convolutional networks (DenseNets), to obtain visual representation from color images. In addition, a new 3D lip physiologic feature based on the position and structure of facial muscles was extracted to represent the similarity of the way people speak. The color image feature and 3D lip geometric-physiologic feature were coupled together in the last fully-connected layer of DenseNets. The experimental results show that DenseNets can handle spatial-temporal information of a whole image sequence and the lip feature integrating our proposed 3D geometric-physiological feature is sufficient to improve the recognition rate by as much as 3.91% (from 94.84%, with the color images only, to 98.75%).
State-of-the-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials

With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
An Embedded Programmable Processor for Compressive Sensing Applications

An application specific programmable processor is designed based on the analysis of a set of greedy recovery Compressive Sensing (CS) algorithms. The solution is flexible and customizable for a wide range of problem dimensions, as well as algorithms. The versatility of the approach is demonstrated by implementing Orthogonal Matching Pursuits, Approximate Messaging Passing and Normalized Iterative Hard Thresholding algorithms, all using a high-level language. Transported Triggered Architecture (TTA) framework is employed for the efficient implementation of macro operations shared by the algorithms. The performance of the CS algorithms on ARM Cortex-A15 and NIOS II processors has also been investigated, and empirical comparisons are presented. The flexible hardware design implemented on an FPGA achieves up to 7.80Ksample/s recovery at a power dissipation of 42$\mu$J/sample and beats both ARM and NIOS in total power consumption.