Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiovascular risk and work stress in biomedical researchers in China: An observational, big data study protocol

Introduction: Internet technologies could strengthen data collection and integration and have been used extensively in public health research. It is necessary to apply this technology to further investigate the behaviour and health of biomedical researchers. A browser-based extension was developed by researchers and clinicians to promote the collection and analysis of researchers' behavioural and psychological data. This protocol illustrates an observational study aimed at (1) characterising the health status of biomedical researchers in China and assessing work stress, job satisfaction, role conflict, role ambiguity, and family support; (2) identifying the association between work, behaviour, and health; and (3) investigating the association between behaviour and mental status. Our findings will contribute to the understanding of the influences of job, work environment, and family support on the mental and physical health of biomedical researchers. Methods and analysis: This is a prospective observational study; all candidates will be recruited from China. Participants will install an extension on their Internet browsers, which will collect data when they are accessing PubMed. A web-based survey will be sent to the user interfaces every 6 months that will involve sociodemographic variables, perceived stress scale, job satisfaction scale, role conflict and ambiguity scale, and family support scale. Machine-learning algorithms will analyse the data generated during daily access. Ethics and dissemination: This study received ethical approval from the ethics committee of the Shanghai Children's Medical Centre (reference number SCMCIRB-K2018082). Study results will be disseminated through peer-reviewed publications and conference presentations.
Study on the Effect of Stress and Rhetorical Structure on College English Discourse Listening

From the perspective of “Schema theory” and “Rhetorical structure theory”, the essay was discussed the effects of stress and rhetorical structure on English discourse listening. Through the means of action research, the authors briefly analyzed the process of teaching intervention and demonstrated the hypotheses with the research data.
Semi-supervised few-shot learning for medical image segmentation

Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such models in a low-data regime highly increases the risk of overfitting. Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a few labeled examples. In this context, a segmentation model is trained on episodes, which represent different segmentation problems, each of them trained with a very small labeled dataset. In this work, we propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations, which ultimately results in better generability to unseen tasks. We demonstrate the efficiency of our method in the task of skin lesion segmentation in two publicly available datasets. Furthermore, our approach is general and model-agnostic, which can be combined with different deep architectures.
PENGEMBANGAN POTENSI EKOWISATA BERBASIS MASYARAKAT DI NAGARI KANDANG BARU KABUPATEN SIJUNJUNG PROPINSI SUMATERA BARAT

Kandang Baru is one of 61 development villages in Sijunjung Regency, West Sumatra Province. The location is very strategic on the trans-Sumatra road, which is the main route connecting the provinces of West Sumatra and Jambi so that it supports the potential and bright prospects in developing economic and socio-cultural development, especially in the tourism sector. This village has good tourism and natural resource potential but has not been exploited either by the government or the local community. With the 2019 PPM Andalas University KKN activities aimed at developing human resources and community empowerment efforts through various creative and innovative work programs, it is expected that the potential can be developed. This activity is carried out with a causal method, which is to carry out work programs based on a SWOT analysis of problems that occur, such as low levels of education, low welfare of the population, conditions of primary schools that are expected, and so on. The results of this activity are summarized in physical and non-physical forms. Physically KKN students succeeded in initiating the construction of the Bukit Capang Tigo geopark tourism area gate (BCT) and have stood beautifully in the area, holding a reading park for elementary students by utilizing the nagari library and other programs. Non-physically, character building is carried out through tutoring, fostering camp tahfidz, and non-physical tourism development in BCT geopark tourism promotion by utilizing social media @KandangBaru Traveling through KKN activities conducted for 40 days, students have succeeded in contributing to Kandang New as a form of community service.
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE

Learning Enabled Components (LECs) are widely being used in a variety of perceptions based autonomy tasks like image segmentation, object detection, end-to-end driving, etc. These components are trained with large image datasets with multimodal factors like weather conditions, time-of-day, traffic-density, etc. The LECs learn from these factors during training, and while testing if there is variation in any of these factors, the components get confused resulting in low confidence predictions. Those images with factor values, not seen, during training are commonly referred to as Out-of-Distribution (OOD). For safe autonomy, it is important to identify the OOD images, so that a suitable mitigation strategy can be performed. Classical one-class classifiers like SVM and SVDD are used to perform OOD detection. However, multiple labels attached to images in these datasets restrict the direct application of these techniques. We address this problem using the latent space of the $\beta$ -Variational Autoencoder ($\beta$ -VAE). We use the fact that compact latent space generated by an appropriately selected $\beta$ - VAE will encode the information about these factors in a few latent variables, and that can be used for quick and computationally inexpensive detection. We evaluate our approach on the nuScenes dataset, and our results show the latent space of $\beta$ - VAE is sensitive to encode changes in the values of the generative factor.
IoT based Smart Home Testbed using MQTT Communication Protocol

Technology is developing day by day in the world. In addition, developing technologies bring innovations and conveniences to all areas of life. However, ensuring the continuity of these innovations brought by technology reveals different problems. Smart home approach, which increases the quality of human living areas, is one of the most popular working subjects of recent times. In a smart home, built with the Internet of Things technology products, it is very important that the sensors and control devices communicate in a safer way and work in a coordinated manner to ensure the ecosystem's continuity in a safer way. In this study, an IoT based smart home testbed was realized by using MQTT communication protocol which one of the most used IoT communication protocols. With the developed system, the control of the smart home and the operating performance of the system were controlled with the mobile application. The results obtained in the light of the data provided by the test show that the system developed with the MQTT communication protocol can successfully ensure data flow and control in Smart Home applications.
PENGARUH GROWTH OPPORTUNITY, FINANCIAL DISTRESS, CEO RETIREMENT TERHADAP KONSERVATISME AKUNTANSI

This research aims to examine and analyze the influence of growth opportunity, financial distress, and CEO retirement to the accounting conservatism at manufacturing companies listed on the Indonesia Stock Exchange. The population of the research is the manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the period of 2013-2017. This research used a purposive sampling method in the selection of the sample based on predetermined criteria. Based on these criteria, obtained samples as much as 37 companies during a five years period of observation. The data used a secondary data. The data collection method used a documentation method. This research using multiple linear regression analysis and hypothesis testing using t-test, F-test and the determination of the coefficient. The results of this research indicate some of the factors that influences positively significant to accounting conservatism, such as growth opportunity and financial distress. CEO retirement has influences negatively significant to accounting conservatism.
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach

For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great performances for the RUL prediction. However, the conventional LSTM network only uses the learned features at last time step for regression or classification, which is not efficient. Besides, some handcrafted features with domain knowledge may convey additional information for the prediction of RUL. It is thus highly motivated to integrate both those handcrafted features and automatically learned features for the RUL prediction. In this article, we propose an attention-based deep learning framework for machine's RUL prediction. The LSTM network is employed to learn sequential features from raw sensory data. Meanwhile, the proposed attention mechanism is able to learn the importance of features and time steps, and assign larger weights to more important ones. Moreover, a feature fusion framework is developed to combine the handcrafted features with automatically learned features to boost the performance of the RUL prediction. Extensive experiments have been conducted on two real datasets and experimental results demonstrate that our proposed approach outperforms the state-of-the-arts.
Study on synergistic action of Coriandrum sativum seed extracts on antibiotics against multidrug resistant P. aeruginosa

Increase in resistance of antibiotic against P. aeruginosa, the antibiotic treatment therapy fail to exterminate the infection. The approach of new strategy is must to deal with multidrug resistant P. aeruginosa. In the present study total 300 isolates from different clinical samples (Urine, Blood, Sputum, and Pus) were studied for isolation, identification and analysis of resistance pattern against 22 antibiotics. Total 5 most resistant isolates (U004, P017, P078, U105, and U237) were screened to search for synergistic approach of Coriandrum sativum extract prepared by both Soxhlet method and Maceration process in different solvents; petroleum ether, chloroform, acetone, methanol and water. Encouraging results were obtained where HDME extract could synergise the activity of total 8 antibiotics that are; MZ, CPZ, CTX, PB, LE, DOR, AZ and CAZ. Thus the present study provides and landmark approach to deal with multidrug resistant P. aeruginosa by synergistic action of antibiotic and Coriandrum sativum extract.
Redistribution Systems and PRAM

Redistribution systems iteratively redistribute mass between groups under the control of rules. PRAM is a framework for building redistribution systems. We discuss the relationships between redistribution systems, agent-based systems, compartmental models and Bayesian models. PRAM puts agent-based models on a sound probabilistic footing by reformulating them as redistribution systems. This provides a basis for integrating agent-based and probabilistic models. \pram/ extends the themes of probabilistic relational models and lifted inference to incorporate dynamical models and simulation. We illustrate PRAM with an epidemiological example.
Depth Estimation by Learning Triangulation and Densification of Sparse Points for Multi-view Stereo

Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high computational cost which impedes practical adoption. Distinct from cost volume approaches, we propose an efficient depth estimation approach by first (a) detecting and evaluating descriptors for interest points, then (b) learning to match and triangulate a small set of interest points, and finally (c) densifying this sparse set of 3D points using CNNs. An end-to-end network efficiently performs all three steps within a deep learning framework and trained with intermediate 2D image and 3D geometric supervision, along with depth supervision. Crucially, our first step complements pose estimation using interest point detection and descriptor learning. We demonstrate that state-of-the-art results on depth estimation with lower compute for different scene lengths. Furthermore, our method generalizes to newer environments and the descriptors output by our network compare favorably to strong baselines.
What Are Friends for? The Effect of Geographic Proximity on Primary Turnout in Gubernatorial Elections

“Friends and neighbors” voting is the tendency of candidates to earn a higher than expected share of the vote in their home counties and states. This hometown effect has been witnessed across an array of elected offices and time periods, prompting researchers to examine how local candidates impact voters’ turnout decisions and electoral choices. This paper focuses on the effect of hometown candidates on turnout using voter rolls and histories from Ohio and Georgia during the 2018 gubernatorial primary elections. These administrative data allow us to observe the decisions of all registered voters in both of these contests along with, importantly, their prior turnout history. We find a consistent relationship between the presence of a hometown candidate and turnout decisions; however, this effect is conditional on voters’ previous vote histories in primaries. The effect is strongest among those who occasionally vote in elections, smaller for those who habitually vote, and negligible for those who have never participated before. These results contribute to our understanding of how the presence of local candidates translates into support for their campaigns, and provide evidence that the size of this effect is not the same for all voters.
Least Square Channel Estimation for Image Transmission with OFDM Over Fading Channel

Wireless communication is the most effective communication to convey audio or video information among the population. It enables the masses to connect throughout the world. Wireless technologies improve the lifestyle of individuals in rural and poor communication areas. In this view, the quality of a reliable signal can be enhanced by minimizing carrier interference. In this paper bit error rate of an image, signal is transmitted over fading channel is analyzed using orthogonal frequency multiplexing and channel estimation technique. An Orthogonal Frequency Multiplexing (OFDM) provides prominent bandwidth effectiveness and improved immunity to the fading environments. In OFDM, the data is modulated using multiple numbers of subcarriers that are orthogonal to each other. A cyclic prefix is infixed between OFDM symbols to annihilate the inter symbol interference (ISI) and inter-carrier interference (ICI). The least square channel estimation method is used to minimize the effect of multipath fading. An image, signal is modulated using BPSK, QPSK, 16QAM and 64QAM digital modulation schemes with OFDM and channel estimation and transmitted over AWGN and fading channel. The objective of this work is to improve the signal to noise ratio by reducing interference.
Access Regulation and Broadband Deployment: Evidence from a Worldwide Dataset

Abstract This paper explores whether the differences in national regulatory policy practices explain gaps in the fixed broadband uptake across countries worldwide. In doing this, we consider a dataset covering 107 developed and developing countries during 2004–2015. Based on data about regulatory practices gathered from several relevant sources, we compute an index to quantify the degree of regulation in the fixed telecommunication segment. Overall, our findings exhibit clearly an inverted U-shape relationship, meaning that high degrees of regulation harm broadband investment. However, isolating the behavior of the least developing countries from the rest of our worldwide panel, we obtain different results. In fact, access regulation is a relatively recent practice in many developing countries. Moreover, the use of this novel data set has allowed us to investigate the specific impact of each one of these regulatory policies. We argue that most findings are in line with the theoretical predictions.
A Case-Control Study on Dietary Quality and Risk for Coronary Heart Disease in Korean Men

【The purpose of this study was to investigate the association between dietary quality and the risk of coronary heart disease (CBD) among Korean men by a case-control study. Methods: The cases were 108 male patients with a first acute myocardial infarction or a new diagnosis of angina pectoris who were admitted to a university teaching hospital in Seoul. Controls were 142 age-matched patients admitted to the departments of ophthalmology and orthopedic surgery at the same hospital. Dietary information was assessed by a nutritionist using a semiquantitative food frequency questionnaire (FFQ), and dietary quality index (DQI) scores were calculated. Results: The intakes of total fat and cholesterol and body mass index (BMI) in cases were significantly higher than those in controls. The mean DQI scores were 8.6 for the cases and 9.4 for the controls. A higher DQI, which was indicative of a better quality diet, was inversely associated with the risk of CHD when comparing the highest to lowest quatiles, but borderline significant (odd ratios 0.60, 95% confidence interval 0.25-1.39, P for trend ＝ 0.05). Conclusions: Our findings suggest that better dietary quality may reduce the risk of CHD among Korean men. 】
Efficiently Calibrating Cable-Driven Surgical Robots With RGBD Sensing, Temporal Windowing, and Linear and Recurrent Neural Network Compensation

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as backlash, stretch, and hysteresis. We propose a novel approach to efficiently calibrate a dVRK by placing a 3D printed fiducial coordinate frame on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling effects between joints and history-dependent effects, we analyze data from sampled trajectories and consider 13 modeling approaches using LSTM recurrent neural networks and linear models with varying temporal window length to provide corrective feedback. With the proposed method, data collection takes 31 minutes to produce 1800 samples and model training takes less than a minute. Results suggest that the resulting model can reduce the mean tracking error of the physical robot from 2.96mm to 0.65mm on a test set of reference trajectories. We evaluate the model by executing open-loop trajectories of the FLS peg transfer surgeon training task. Results suggest that the best approach increases success rate from 39.4% to 96.7% comparable to the performance of an expert surgical resident. Supplementary material, including 3D-printable models, is available at https://sites.google. com/berkeley.edu/surgical-calibration.
Novel Optimized Shape and Topology for Slotless Windings in BLDC Machines

Slotless windings are commonly used in small size high-speed electrical brushless dc machines. Unlike slotted windings, forced to follow the direction of slots, slotless windings can be freely arranged in the airgap. This represents several opportunities for optimization. Nevertheless, very little research is performed in this domain. This article intends to challenge the traditional way of manufacturing windings. Indeed, most of the windings are made with isolated wire of constant section. However, it is demonstrated here that the performances of a motor can be significantly increased by optimizing the shape of the winding and by selecting nonconstant wire sections. A prototype of the new topology has been manufactured and measurements have demonstrated an increase of the motor constant from 18% to 24% compared to a traditional slotless winding.
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections

Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they comprise. In this work, we propose metrics of diversity, density, and homogeneity that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. We conduct a series of simulations to verify that each metric holds desired properties and resonates with human intuitions. Experiments on real-world datasets demonstrate that the proposed characteristic metrics are highly correlated with text classification performance of a renowned model, BERT, which could inspire future applications.
Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding

This paper proposes a novel approach to nonlinear state-feedback control design that has three main advantages: (i) it ensures exponential stability and $ \mathcal{L}_2 $-gain performance with respect to a user-defined set of reference trajectories, and (ii) it provides constructive conditions based on convex optimization and a path-integral-based control realization, and (iii) it is less restrictive than previous similar approaches. In the proposed approach, first a virtual representation of the nonlinear dynamics is constructed for which a behavioral (parameter-varying) embedding is generated. Then, by introducing a virtual control contraction metric, a convex control synthesis formulation is derived. Finally, a control realization with a virtual reference generator is computed, which is guaranteed to achieve exponential stability and $ \mathcal{L}_2 $-gain performance for all trajectories of the targeted reference behavior. Connections with the linear-parameter-varying (LPV) theory are also explored showing that the proposed methodology is a generalization of LPV state-feedback control in two aspects. First, it is a unified generalization of the two distinct categories of LPV control approaches: global and local methods. Second, it provides rigorous stability and performance guarantees when applied to the true nonlinear system, while such properties are not guaranteed for tracking control using LPV approaches.