Miracle-dz/UniFAR-FTUs huggingface dataset:
Employee Engagement Millennial Generation (Gen Y) Reviewed from Leadership Factors, Internal Communication, and Organization Climate

This research is to examine the factors of employee engagement of millennial generation (GEN Y) to the company in terms of leadership, internal communication and organizational climate. This study uses a quantitative approach to test the proposed hypothesis. The subjects of this study were all employees in the millennial age group at PT. MNC Networks with a total sample of 90 people. The study uses a data collection method with a Likert scale that provides five answer choices. Processing and analyzing research data using PLS (Partial Least Square) software version 3.0. The results of this study indicate that 14% o leadership factors have an influence on the organizational climate, with a tcount of 2.866 and a P value of 0.005. Leadership apparently also has a direct influence on employee engagement in the amount of 22.90%, with a tcount of 4.866 and a P value of 0.000, while for internal communication factors also affect the organizational climate of 16.30%, with a tcount of 3.069 and a P value of 0.002, but for internal communication does not have a direct influence on employee engagement where the significance test results only show 0.10%, with a tcount of 0.370 and a P value of 0.712. Factors of organizational climate turned out to have an influence on employee engagement that is equal to 10.90%, with a tcount of 2.739 and a P value of 0.007. The structural model formed for organizational climate = 0.385 * leadership + 0.415 * internal communication, while the structural model for employee engagement = 0.522 * leadership-0.041 * internal communication + 0.328 * organizational climate.
Use of Dietary Frying Palm Oil in Broiler Chickens Rations their Growth Performance and Carcass Traits

A study was conducted to investigate the Use of Dietary Frying Palm Oil (FPO) in Broiler Chickens Rations on body weight gain (BWG), feed intake (FI) and feed conversion ratio (FCR) in broiler chicks, haematological values, serum metabolite values and serum electrolytes and enzyme activities values. A total of 96 of the Ross 308 chicks were distributed into 4 treatments with 4 replicates/ treatment (6 birds/pen) in floor brooder pens. Four treatment diets were fed from d 0 to 42: T1 (negative control without FPO), diets T2, T3 and T4 (negative control supplemented with 3, 6 and 9% Frying Palm Oil, respectively). Weekly BWG, FI and FCR were evaluated for six weeks. Feed intake per bird did not differ among the treatments during any trial periods. THE lowest BWG in chicks fed T4 and highest BWG in T3 were observed both in wk 6 (P < 0.05). FPO treatments significantly influenced FCR in all weeks overall. T3 had the highest FCR (P < 0.05), whereas the rest of the treatment diets did not differ from control diets, all levels of FPO had FCR similar to the negative control (P < 0.05). Results of the present experiment indicated that the middle level of FPO showed better BWG and the highest level of FPO showed lower BWG. haematological values red blood cell (RBC), white blood cell (WBC) and Packed Cell Volume (PCV). In 6 weeks, there was no significant difference (P < 0.05) in serum electrolytes and enzyme activities values in alanine aminotransferase (ALT), aspartate aminotransferase (AST) of chicks treated with vegetable oils compared to the control group. Total protein (TP) and urea in all treated chicks for six weeks were within the normal range were no significant increase (P < 0.05) compared to the control groups. Tenderness was affected significantly (p < 0.05) economically appraised values were profitability ratio of the test T3 was the higher of the test groups. Recycling of frying oil in poultry feed, especially frying olein oil, after ensuring that it is free from harmful substances. Adding 3 6% of the oil improves the carcass, the feed intake, feed conversion efficiency and the profitability margin.
Water Uptake by Evaporating pMDI Aerosol Prior to Inhalation Affects Both Regional and Total Deposition in the Respiratory System

As pulmonary drug deposition is a function of aerosol particle size distribution, it is critical that the dynamics of particle formation and maturation in pMDI sprays in the interim between generation and inhalation are fully understood. This paper presents an approach to measure the evaporative and condensational fluxes of volatile components and water from and to solution pMDI droplets following generation using a novel technique referred to as the Single Particle Electrodynamic Lung (SPEL). In doing so, evaporating aerosol droplets are shown capable of acting as condensation nuclei for water. Indeed, we show that the rapid vaporisation of volatile components from a volatile droplet is directly correlated to the volume of water taken up by condensation. Furthermore, a significant volume of water is shown to condense on droplets of a model pMDI formulation (hydrofluoroalkane (HFA), ethanol and glycerol) during evaporative droplet ageing, displaying a dramatic shift from a core composition of a volatile species to that of predominantly water (non-volatile glycerol remained in this case). This yields a droplet with a water activity of 0.98 at the instance of inhalation. The implications of these results on regional and total pulmonary drug deposition are explored using the International Commission of Radiological Protection (ICRP) deposition model, with an integrated semi-analytical treatment of hygroscopic growth. Through this, droplets with water activity of 0.98 upon inhalation are shown to produce markedly different dose deposition profiles to those with lower water activities at the point of inspiration.
Spoken Language Identification on Local Language using MFCC, Random Forest, KNN, and GMM

Spoken language identification is a field of research that is already being done by many people. There are many techniques proposed for doing speech processing, such as Support Vector Machines, Gaussian Mixture Models, Decision Trees, and others. This paper will use the system using the MelFrequency Cepstral Coefficient (MFCC) features of speech input signal, use Random Forest (RF), Gaussian Mixture Model (GMM), and K-Nearest Neighbor (KNN) as a classifier, use the 3s, 10s, and 30s as scoring method, and use dataset that consists of Javanese, Sundanese, and Minang languages which are traditional languages from Indonesia. K-Nearest Neighbor has 98.88% of accuracy for 30s of speech and followed by Random Forest that has 95.55% of accuracy for 30s of speech, GMM has 82.24% of accuracy. Keywords—Gaussian mixture model; random forest; K-Nearest Neighbor; spoken language recognition; MFCC; GMM; KNN
Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

Abstract Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pretreatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
Privileged Zero-Shot AutoML

This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach. Given a new dataset and a well-defined machine learning task, humans begin by reading a description of the dataset and documentation for the algorithms to be used. This work is the first to use these textual descriptions, which we call privileged information, for AutoML. We use a pre-trained Transformer model to process the privileged text and demonstrate that using this information improves AutoML performance. Thus, our approach leverages the progress of unsupervised representation learning in natural language processing to provide a significant boost to AutoML. We demonstrate that using only textual descriptions of the data and functions achieves reasonable classification performance, and adding textual descriptions to data meta-features improves classification across tabular datasets. To achieve zero-shot AutoML we train a graph neural network with these description embeddings and the data meta-features. Each node represents a training dataset, which we use to predict the best machine learning pipeline for a new test dataset in a zero-shot fashion. Our zero-shot approach rapidly predicts a high-quality pipeline for a supervised learning task and dataset. In contrast, most AutoML systems require tens or hundreds of pipeline evaluations. We show that zero-shot AutoML reduces running and prediction times from minutes to milliseconds, consistently across datasets. By speeding up AutoML by orders of magnitude this work demonstrates real-time AutoML.
FOVQA: Blind Foveated Video Quality Assessment

Previous blind or No Reference (NR) Image / video quality assessment (IQA/VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model, called FOVQA, which is based on new models of space-variant natural scene statistics (NSS) and natural video statistics (NVS). Specifically, we deploy a space-variant generalized Gaussian distribution (SV-GGD) model and a space-variant asynchronous generalized Gaussian distribution (SV-AGGD) model of mean subtracted contrast normalized (MSCN) coefficients and products of neighboring MSCN coefficients, respectively. We devise a foveated video quality predictor that extracts radial basis features, and other features that capture perceptually annoying rapid quality fall-offs. We find that FOVQA achieves state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as compared with other leading Foveated IQA / VQA models. we have made our implementation of FOVQA available at: https://live.ece. utexas.edu/research/Quality/FOVQA.zip.
VEGN: Variant Effect Prediction with Graph Neural Networks

Genetic mutations can cause disease by disrupting normal gene function. Identifying the disease-causing mutations from millions of genetic variants within an individual patient is a challenging problem. Computational methods which can prioritize disease-causing mutations have, therefore, enormous applications. It is well-known that genes function through a complex regulatory network. However, existing variant effect prediction models only consider a variant in isolation. In contrast, we propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants. The graph is created by assigning variants to genes and connecting genes with an gene-gene interaction network. In this context, we explore an approach where a gene-gene graph is given and another where VEGN learns the gene-gene graph and therefore operates both on given and learnt edges. The graph neural network is trained to aggregate information between genes, and between genes and variants. Variants can exchange information via the genes they connect to. This approach improves the performance of existing state-of-the-art models.
On Kaczmarz method with oblique projection for solving large overdetermined linear systems

In this paper, an extension of Kaczmarz method, the Kaczmarz method with oblique projection (KO), is introduced and analyzed. Using this method, a number of iteration steps to solve the over-determined systems of linear equations are significantly reduced, and the the computing time is much saved, especially for those problems that contain some linear equations with near-linear correlation. Simultaneously, a randomized version–randomized Kaczmarz method with oblique projection (RKO) is established. The convergence proofs of these two methods are given and numerical experiments show the effectiveness of the two methods for uniformly distributed random data. Especially when the system has correlated rows, the improvement of experimental results is very prominent.
Implementation of Nudged Elastic Band in CRYSTAL

Locating transition states and energies is a very important problem in many areas of chemistry. The rate of chemical reactions and diffusion events are, in part, determined by the energy barrier between the reactants and the products. The Nudged Elastic Band algorithm locates transition states using knowledge of the end states (the reactants and products) along with the energy and its first derivatives. A parallel version of this algorithm has been implemented in CRYSTAL. The method has been rigorously tested to ensure that it performs effectively and does not interfere with any other functionality within CRYSTAL.
Transverse magnetic routing of light emission in hybrid plasmonic-semiconductor nanostructures: Towards operation at room temperature

Lars Klompmaker,1, ∗ Alexander N. Poddubny,2 Eyüp Yalcin,1 Leonid V. Litvin,3 Ralf Jede,3 Grzegorz Karczewski,4 Sergij Chusnutdinow,4 Tomasz Wojtowicz,5 Dmitri R. Yakovlev,1, 2 Manfred Bayer,1, 2 and Ilya A. Akimov1, 2 1Experimentelle Physik 2, Technische Universität Dortmund, 44221 Dortmund, Germany 2Ioffe Institute, Russian Academy of Sciences, 194021 St. Petersburg, Russia 3Raith GmbH, 44263 Dortmund, Germany 4Institute of Physics, Polish Academy of Sciences, PL-02668 Warsaw, Poland 5International Research Centre MagTop, Institute of Physics, Polish Academy of Sciences, PL-02668 Warsaw, Poland (Dated: June 28, 2021)
HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition

Previous methods for skeleton-based gesture recognition mostly arrange the skeleton sequence into a pseudo picture or spatial-temporal graph and apply deep Convolutional Neural Network (CNN) or Graph Convolutional Network (GCN) for feature extraction. Although achieving superior results, these methods have inherent limitations in dynamically capturing local features of interactive hand parts, and the computing efficiency still remains a serious issue. In this work, the self-attention mechanism is introduced to alleviate this problem. Considering the hierarchical structure of hand joints, we propose an efficient hierarchical self-attention network (HAN) for skeleton-based gesture recognition, which is based on pure self-attention without any CNN, RNN or GCN operators. Specifically, the joint self-attention module is used to capture spatial features of fingers, the finger self-attention module is designed to aggregate features of the whole hand. In terms of temporal features, the temporal self-attention module is utilized to capture the temporal dynamics of the fingers and the entire hand. Finally, these features are fused by the fusion self-attention module for gesture classification. Experiments show that our method achieves competitive results on three gesture recognition datasets with much lower computational complexity.
Discrimination of mushroom disease‐related mould species based solely on unprocessed chromatograms

Agaricus bisporus A15 species (bisporic button mushroom) and its two main mould diseases (Mycogone perniciosa and Trichoderma aggressivum) were analysed using headspace solid‐phase microextraction sampling coupled with gas chromatograpy–mass spectrometry analysis. The presence or absence of mushroom disease‐related moulds can easily be detected and monitored in the air by headspace solid‐phase microextraction gas chromatograpy–mass spectrometry via their emitted microbial volatile organic compounds. Detrended fluctuation analysis (DFA) was first applied to distinguish different mould samples based on their unprocessed total ion chromatograms, which describe the air polluted by microbial volatile organic compounds. A two‐dimensional plot, generated from the results of DFA, visualizes the relationship of the analysed samples. The pattern found by DFA was compared with the pattern found by the traditionally applied principal component analysis. Cluster analysis (using Euclidean distance and Ward's method) of the principal component analysis scores and DFA coefficients showed characteristic groupings. A methodology is introduced, which uses the unprocessed chromatogram, without any time‐consuming feature extraction and can indicate the presence of infected samples. The proposed methodology is able to give a support to the mushroom growers to indicate different mushroom disease‐related infection via air monitoring. Additionally, DFA is a sample‐independent and universal method. Hence, it can be also applied to distinguish unprocessed chromatograms produced by any other analytical equipment. Copyright © 2016 John Wiley & Sons, Ltd.
Space–Charge Layer Effect at Interface between Oxide Cathode and Sulfide Electrolyte in All-Solid-State Lithium-Ion Battery

We theoretically elucidated the characteristics of the space–charge layer (SCL) at interfaces between oxide cathode and sulfide electrolyte in all-solid-state lithium-ion batteries (ASS-LIBs) and the effect of the buffer layer interposition, for the first time, via the calculations with density functional theory (DFT) + U framework. As a most representative system, we examined the interfaces between LiCoO2 cathode and β-Li3PS4 solid electrolyte (LCO/LPS), and the LiCoO2/LiNbO3/β-Li3PS4 (LCO/LNO/LPS) interfaces with the LiNbO3 buffer layers. The DFT+U calculations, coupling with a systematic procedure for interface matching, showed the stable structures and the electronic states of the interfaces. The LCO/LPS interface has attractive Li adsorption sites and rather disordered structure, whereas the interposition of the LNO buffer layers forms smooth interfaces without Li adsorption sites for both LCO and LPS sides. The calculated energies of the Li-vacancy formation and the Li migration reveal that subsurfa.
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS), user preferences and the system's fairness status are constantly changing over time. Existing fairness-aware recommenders mainly consider fairness in static settings. Directly applying existing methods to IRS will result in poor recommendation. To resolve this problem, we propose a reinforcement learning based framework, FairRec, to dynamically maintain a long-term balance between accuracy and fairness in IRS. User preferences and the system's fairness status are jointly compressed into the state representation to generate recommendations. FairRec aims at maximizing our designed cumulative reward that combines accuracy and fairness. Extensive experiments validate that FairRec can improve fairness, while preserving good recommendation quality.
MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation

Estimation of the precision matrix (or inverse covariance matrix) is of great importance in statistical data analysis and machine learning. However, as the number of parameters scales quadratically with the dimension $p$, computation becomes very challenging when $p$ is large. In this paper, we propose an adaptive sieving reduction algorithm to generate a solution path for the estimation of precision matrices under the $\ell_1$ penalized D-trace loss, with each subproblem being solved by a second-order algorithm. In each iteration of our algorithm, we are able to greatly reduce the number of variables in the {problem} based on the Karush-Kuhn-Tucker (KKT) conditions and the sparse structure of the estimated precision matrix in the previous iteration. As a result, our algorithm is capable of handling datasets with very high dimensions that may go beyond the capacity of the existing methods. Moreover, for the sub-problem in each iteration, other than solving the primal problem directly, we develop a semismooth Newton augmented Lagrangian algorithm with global linear convergence rate on the dual problem to improve the efficiency. Theoretical properties of our proposed algorithm have been established. In particular, we show that the convergence rate of our algorithm is asymptotically superlinear. The high efficiency and promising performance of our algorithm are illustrated via extensive simulation studies and real data applications, with comparison to several state-of-the-art solvers.
Bunch Expansion as a Cause for Pulsar Radio Emissions

Electromagnetic waves due to electron–positron clouds (bunches), created by cascading processes in pulsar magnetospheres, have been proposed to explain the pulsar radio emission. In order to verify this hypothesis, we utilized for the first time Particle-in-Cell (PIC) code simulations to study the nonlinear evolution of electron–positron bunches dependant on the initial relative drift speeds of electrons and positrons, plasma temperature, and distance between the bunches. For this sake, we utilized the PIC code ACRONYM with a high-order field solver and particle weighting factor, appropriate to describe relativistic pair plasmas. We found that the bunch expansion is mainly determined by the relative electron–positron drift speed. Finite drift speeds were found to cause the generation of strong electrostatic superluminal waves at the bunch density gradients that reach up to E ∼ 7.5 × 105 V cm−1 (E/(m e c ω p e −1) ∼ 4.4) and strong plasma heating. As a result, up to 15% of the initial kinetic energy is transformed into the electric field energy. Assuming the same electron and positron distributions, we found that the fastest (in the bunch reference frame) particles of consecutively emitted bunches eventually overlap in momentum (velocity) space. This overlap causes two-stream instabilities that generate electrostatic subluminal waves with electric field amplitudes reaching up to E ∼ 1.9 × 104 V cm−1 (E/(m e c ω p e −1) ∼ 0.11). We found that in all simulations the evolution of electron–positron bunches may lead to the generation of electrostatic superluminal or subluminal waves, which, in principle, can be behind the observed electromagnetic emissions of pulsars in the radio wave range.