Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimeter-Wave AlGaN/GaN HEMTs With 43.6% Power-Added-Efficiency at 40 GHz Fabricated by Atomic Layer Etching Gate Recess

Low damage atomic layer etching (ALE) gate recess is developed for fabrication of millimeter-wave AlGaN/GaN high-electron-mobility transistors (HEMTs). Plasma ion induced bombardments to the AlGaN barrier is effectively suppressed by the ALE recess, contributing to a well-controlled recessed surface morphology. The suppressed lattice damage to AlGaN/GaN heterostructure is also reflected by a significantly reduced gate leakage as well as an invisible threshold voltage shift associated with damage induced traps. With a 0.15-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> T-gate fabrication technology, a high power-gain cutoff frequency <inline-formula> <tex-math notation="LaTeX">${f}_{\text {MAX}}$ </tex-math></inline-formula> of 205 GHz has been achieved. The ALE-recessed AlGaN/GaN HEMTs exhibits a record high power-added-efficiency (PAE) of 43.6% at 40 GHz in a continuous-wave mode. The associated gain and output power density are also remarkably improved compared with controlled HEMTs with conventional gate recess process.
TREATMENT GAP FOR CO-MORBID DEPRESSION IN MEDICAL OUTPATIENTS WITH HYPERTENSION: A CROSS-SECTIONAL HOSPITAL-BASED STUDY

Introduction: Despite an increasing number of studies reporting high levels of co-morbidity between depression and hypertension in high-income countries, there is limited information on co-morbid depression among hypertensive patients in low- and middle-income countries. Objectives: The aim of this study was to determine the prevalence of depression, associated factors and treatment gap among hypertensive patients attending outpatient follow-up at a referral hospital in Ethiopia. Methods: A hospital-based cross-sectional study was conducted in 302 consecutive outpatient attendees with hypertension. Probable depression was measured using an interviewer administered depression scale (the Patient Health Questionnaire; PHQ-9) previously validated for the Ethiopian setting for a cut-off of 9 and above. Contextualised standardized measures of medication adherence, alcohol and khat use were used. Clinical information was extracted from medical notes, augmented by assessment of body mass index. Univariate and multivariable analyses were carried out to assess factors associated with depression. Results: The prevalence of probable depression among participants was 22.2%. The treatment gap was 91%. Only 9% (n=6) had received any treatment for depression. Of these, all were prescribed an antidepressant. Female gender, adjusted odds ratio (aOR) 2.6, (95% confidence interval (CI) 1.2, 5.8), being unmarried (aOR 2.0; 95% CI 1.0, 3.7) and having two or more co-morbid medical illnesses (aOR 12.6; 95% CI 4.3, 37.0) were associated independently with depression. There was no association between depression and medication adherence, diet, physical activity or body mass index.
Evaluation of Grass Bales Stored under Cover and Plinth System of Storage

Part of the Plant Sciences Commons, and the Soil Science Commons This document is available at https://uknowledge.uky. edu/igc/23/2-1-2/22 The 23rd International Grassland Congress (Sustainable use of Grassland Resources for Forage Production, Biodiversity and Environmental Protection) took place in New Delhi, India from November 20 through November 24, 2015. Proceedings Editors: M. M. Roy, D. R. Malaviya, V. K. Yadav, Tejveer Singh, R. P. Sah, D. Vijay, and A. Radhakrishna Published by Range Management Society of India
A Semi-Supervised Approach For Identifying Abnormal Heart Sounds Using Variational Autoencoder

Abnormal heart sounds may have diverse frequency characteristics depending upon underlying pathological conditions. Designing a binary classifier for predicting normal and abnormal heart sounds using supervised learning requires a lot of training data, covering different types of cardiac abnormalities. In this paper, we propose a semi-supervised approach to solve the problem. A convolutional Variational Autoencoder (VAE) structure is defined for learning the probability distribution of the spectrogram properties of normal heart sounds. The Kullback-Leibler (KL) divergence between the known prior distribution of the VAE and the encoded distribution is taken as an anomaly score for detecting abnormal heart sounds. The proposed approach is evaluated on open access and in-house datasets of Phonocardiogram (PCG) signals, recorded from normal subjects and patients, having cardiovascular diseases, cardiac murmurs and extra heart sounds. Results show that an improved classification performance is achieved in comparison to the existing approaches.
Testing an Automated Collision Avoidance and Emergency Braking System for Buses

In 2017 the Federal Transit Administration (FTA) awarded Pierce Transit of Lakewood, WA a $1.66 million grant for a bus collision avoidance and mitigation safety research and demonstration project. The project scope includes installation of an advanced technology package, the Pedestrian Avoidance Safety System (PASS) that uses lidar sensors to trigger an automated deceleration and braking system. An “alpha testing” phase included shipping a Pierce Transit bus to Blacksburg, VA for closed-course testing of PASS on Virginia Tech Transportation Institute’s (VTTI’s) Smart Road facility. In addition, VTTI developed a system to observe, measure, and analyze passenger motion during braking events. Following completion of testing at VTTI, the bus will be returned to Pierce Transit. Together with three additional buses currently being outfitted with PASS, all four will be equipped with Transit Event Logging System (TELS) video processers developed by University of Washington’s Smart Transportation Applications & Research (STAR) Lab to analyze PASS accuracy for “false positives” and “false negatives. ” Upon successful completion of in-service engineering testing of the initial four buses, an additional 26 buses will be equipped with PASS and all 30 will be monitored using telematics for a year-long demonstration. This paper discusses project background and organization, describes the PASS being tested, provides an overview of the alpha testing, describes project data collection processes, and reviews the criteria and metrics being used to evaluate the system. The paper concludes with observations about lessons learned to date.
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Abstract. Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error – MAE – of 2.28 %, anomaly correlation coefficient – ACC – of 0.98, root-mean-square error – RMSE – of 5.76 %, normalized RMSE – nRMSE – of 16.15 %, and NSE – Nash–Sutcliffe efficiency – of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics.
ELASTASE ACTIVITY INHIBITION BY THE MOST ACTIVE FRACTION OF STAR FRUIT (AVERRHOA CARAMBOLA L.) LEAVES FROM THREE WEST JAVA REGIONS

Objective: Premature skin aging is caused by increased elastase proteolytic activity, which causes elastin breakdown and disorganization in connectivetissue, reducing elasticity and flexibility, and wrinkling skin. Natural compounds in plants, especially polyphenols, inhibit elastase proteolyticactivity and prevent premature skin aging. Star fruit (Averrhoa carambola L.) leaves contain many polyphenols with antioxidant, anti-inflammatory,hypoglycemic, and antimicrobial activities. However, no studies have shown that A. carambola leaves inhibit elastase proteolytic activity.Methods: This study tested the inhibition of elastase proteolytic activity by the water fractions (WF), ethyl acetate fractions, and n-hexane fractionsof A. carambola leaves from the Depok, Sukabumi, and Subang regions of West Java. Each fraction was tested using a microplate reader, and the totalphenolic and flavonoid content was determined for the most active fraction.Results: The WF of the A. carambola leaves from Depok was the most active fraction, with a half-maximal inhibitory concentration of 160.36 μg/mL.The total phenolic and flavonoid content in the WF was 115.68 mg gallic acid equivalent/g extract and 9.15 mg quercetin equivalent/g extract,respectively.Conclusion: The WF of A. carambola leaves is a natural material that may inhibit elastase proteolytic activity and prevents premature skin aging.
Risk-Parity Optimality Even with Negative Sharpe Ratio Assets

A stopped clock is right twice a day. Similarly, any portfolio allocation is likely to be optimal at least at some point. Risk parity is no stopped clock. The authors derive a general result regarding when and why risk parity is Sharpe ratio optimal, even with negative-Sharpe-ratio assets. This derivation goes beyond the simple observation that risk parity is Sharpe ratio optimal when asset correlations and Sharpe ratios are identical. Based on the analytical result, the authors develop an indicator to describe when risk-parity strategies are likely to be more or less optimal. They also explain how negative-Sharpe-ratio assets can still be an important part of a portfolio—whether it is a risk-parity portfolio or not. Although risk parity and risk balancing in general do not require assumptions about returns, the authors provide guidance regarding how to infer returns that are consistent with the portfolios built from targeting risk. TOPICS: Statistical methods, portfolio theory, portfolio construction Key Findings • Conditions that are generally sufficient for risk-parity portfolios to be Sharpe ratio optimal are provided, beyond the special case of identical cross-asset correlations and Sharpe ratios. From this, an indicator of how likely risk parity is to be Sharpe ratio optimal can be derived. • Based on diversification properties of assets, the generally sufficient conditions for risk-parity portfolios to be Sharpe ratio optimal explain why even negative-return assets can be included in a diversified portfolio. • A method is provided to translate risk targets into a rank ordering of asset returns. With an additional set of assumptions, these rank orderings can be translated into returns for a coherent set of risk targets, risks, and returns.
Impact of Insulin Resistance on Therapeutic Response to Oral Treatment of Chronic Hepatitis C Virus Infection

Background& aim: HCV infection is one of the major health problems in our country. Prevalence of DM is higher among patients with chronic HCV infection. Insulin resistance (IR) is common in such and its impact on sustained virological response (SVR) is not well studied. This work was designed to assess impact of IR on SVR. Patients & methods: Between July 2016 and June 2017; 200 patients with chronic HCV infection were enrolled in a prospective study. Exclusion criteria included decompensated cirrhosis, hepatocellular carcinoma or extrahepatic malignancy, co-infection with HBV or HIV infection. HOMA and IR were assessed at baseline of therapy and 3-months post-therapy. Patients received sofosbuvir and daclatasvir for 3 months (chronic hepatitis) and for 6 months in (liver cirrhosis). Results: Mean age of patients was 49.89 ± 9.01 years, 111 (55.5%) patients were male and 180 (90%) achieved SVR. Baseline IR had insignificant difference between responders and non-responders (93% v. 90%; P = 0.45), while baseline HOMA was significantly higher in non-responders (10.11 ± 3.03 vs. 8.48 ± 2.98; P = 0.01). Also, post-therapy IR had insignificant difference between both groups (73.3% vs. 85%; P = 0.05), while post-therapy HOMA was significantly higher in non-responders (7.12 ± 2.31 vs. 5.06 ± 1.34; P = 0.01). Predictors of non-responders were age (> 40 years), low serum albumin and post-therapy IR. Conclusion: Baseline IR had no impact on SVR but it showed significant improvement in presence of SVR.
Exploring anomalous behaviour detection and classification for insider threat identification

Recently, malicious insider threats represent one of the most damaging threats to companies and government agencies. Insider threat detection is a highly skewed data analysis problem, where the huge class imbalance makes the adaptation of learning algorithms to the real‐world context very difficult. This study proposes a new system for user‐centred machine learning‐based anomaly behaviour and insider threat detection on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious users. Our results show that the proposed system, which is a combination of unsupervised anomaly detection and supervised machine learning methods, can learn from unlabelled data and a very small amount of labelled data. Furthermore, it can generalize to bigger datasets for detecting anomalous behaviours and unseen malicious insiders with a high detection and a low false‐positive rate.
Shifts in phytoplankton species richness and biomass along a latitudinal gradient – consequences for relationships between biodiversity and ecosystem functioning

Summary 1. Analysing phytoplankton community data from 205 small, mainly oligotrophic Swedish lakes along a 13° latitudinal gradient, we found that the duration of the open-water season (DT > 0) was best related to phytoplankton species richness and biomass, probably because DT > 0 can be used as a proxy for nutrient concentrations, as well as for light and temperature conditions in lakes. 2. The relationships between DT > 0 and phytoplankton species richness and biomass were not linear but showed significant shifts, that is, richness suddenly doubled at DT > 0 = 170 days and phytoplankton biomass began to strongly increase at DT > 0 around 220 days. 3. The doubling in species richness corresponded to a change in the seasonal plankton succession from one to two peaks per year, and the shift in biomass corresponded to a shift in nutrient concentrations. 4. The geographical differences in richness and biomass shifts resulted in an exponential biodiversity–ecosystem functioning relationship along a DT > 0 gradient. 5. We conclude that phytoplankton species richness and biomass are driven by different environmental factors, resulting in a nonlinear biodiversity–ecosystem functioning relationship. The shape of the diversity–functioning relationship varies along with variations in environmental drivers, which is of highest importance for ongoing discussions about impacts of global change on biodiversity and ecosystem functioning.
Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier

The electroencephalogram (EEG) signal in the brain–computer interface (BCI) has suffered great cross-subject variability. The BCI system needs to be retrained before each time it is used, which is a waste of resources and time. Thus, it is difficult to generalize a fixed classification method for all subjects. Therefore, the transfer learning method proposed in this article, which combines XDAWN spatial filter and Riemannian Geometry classifier (RGC), can achieve offline cross-subject transfer learning in the P300-speller paradigm. The XDAWN spatial filter is used to enhanced the P300 components in the raw signal as well as reduce its dimensions. Then, the Riemannian Geometry Mean (RGM) is used as the reference matrix to perform the affine transformation of the symmetric positive definite (SPD) covariance matrix calculated from the filtered signal, which makes the data from different subjects comparable. Finally, the RGC is used to obtain the result of transfer learning experiments. The proposed algorithm was evaluated on two datasets (Dataset I from real patients and Dataset II from the laboratory). By comparing with two state-of-the-art and classic algorithms in the current BCI field, Ensemble of Support Vector Machine (E-SVM) and Stepwise Linear Discriminant Analysis (SWLDA), the maximum averaged area under the receiver operating characteristic curve (AUC) score of our algorithm reached 0.836, proving the potential of our proposed algorithm.
How do Chinese travelers experience the Arctic? Insights from a hedonic and eudaimonic perspective

ABSTRACT This study explores the tourism experience of Chinese independent travelers in the Arctic region from the perspective of hedonic and eudaimonic well-being. We examine 12 “informant, influential and interactive” online travel blogs on Qyer.com, the biggest virtual community for Chinese outbound independent travelers. A three-level manual coding process of qualitative data has shown that hedonic Arctic experiences include “gazing at otherness”, “encounter and interaction”, “immersion in the local community”, “bodily feelings”, and “positive affects”. From eudaimonic perspective, visitors “experience challenges” (environmental, physical, and mental) at first, but they learn to “build capacities” and respond to these challenges. After conquering self-limits, Chinese travelers go through “revitalization of the body”, obtain “high spirits” and achieve “enrichment of the mind”. Some of the experiential outcomes can be explained by traditional Chinese value orientations such as self-cultivation, collectivism, and human–environment harmony. The authors recommend Arctic tourism to be positioned as a highly valued, exotic, and extraordinary destination for independent travelers who aim to enrich their mind and soul above material pleasure.
Analyzing Quality Estimation Of English-Hindi Machine Translation System

: Automatically estimating the translation quality is a challenging topic of research in the field of MT. This paper describes the approach used for sentence level quality estimation problem on English-Hindi language pair. The purpose of the translation quality estimation (QE) is to predict a quality for unseen translated text without considering the reference translation. To perform the proposed technique, this submission conceived the quality estimation problem as a supervised learning approach. Feature extraction is an important step for supervised ML based quality estimation, and therefore, in this paper, we experimented with a set of multiple features along with the different ensemble type of learning algorithms. From the experimental results on the test set, we have found that Extra Tree based QE models gain improvements over the other two ensemble regressors. Moreover, the analysis of the performance evaluation measures show that the quality of the translation generated by the MT engine1 was best among all the four different MT engines.
A New Perspective on Raising Energy Efficiency: A Test Based on Market Segmentation*

A comparison of the facts at the micro- and macro-level indicates that although some Chinese enterprises are at the international frontier in terms of micro-level energy efficiency, overall energy efficiency at the industrial and national level is seriously lagging behind. Hypotheses based on a market segmentation framework indicate that this is because market segmentation inhibits energy efficiency through its effect on technical efficiency, scale efficiency and allocation efficiency. This effect has been demonstrated by empirical research based on Chinese provincial panel data.
Lexicogrammatical Features of ELF Speakers in an Asian Students Association Small Talk in Indonesia

—This study examines the nature of English as a Lingua Franca (ELF) interaction among English users whose mother tongues are different. Specifically, this study aims to investigate the lexicogrammatical features evident in a small talk among Asian students. The data are elicited in a mixture of authentic and simulated interactions. As a stimulus for the speakers to talk, they are given topics on the Asian African Conference and its relation to their country, as well as the culture of their country. This study obtained the data by recording the conversations from ASAI (Asian Student Association in Indonesia) small talk. Using lexicogrammatical features proposed by Seidlhofer (2004) and Kirkpatrick (2008), the findings reveal that the speakers produced six lexicogrammatical features which are dropping third person present tense –s, confusing the relative pronouns who and which, omitting definite and indefinite articles, preposition, marking or nonmarking plural, and the use of tag questions form. Although these features are different from features in English standard forms, they are commonly used by other ELF speakers in different studies. Also, these features do not appear to cause misunderstandings in the interaction. This seems to suggest that these users are appropriating the language for their own purposes and the effectiveness of communication.
Handwritten Signature Verification using Deep Learning

: Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%.