Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct radiative effect of aerosols based on PARASOL and OMI satellite observations

Accurate portrayal of the aerosol characteristics is crucial to determine aerosol contribution to the Earth's radiation budget. We employ novel satellite retrievals to make a new measurement‐based estimate of the shortwave direct radiative effect of aerosols (DREA), both over land and ocean. Global satellite measurements of aerosol optical depth, single‐scattering albedo (SSA), and phase function from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) are used in synergy with OMI (Ozone Monitoring Instrument) SSA. Aerosol information is combined with land‐surface bidirectional reflectance distribution function and cloud characteristics from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite products. Eventual gaps in observations are filled with the state‐of‐the‐art global aerosol model ECHAM5‐HAM2. It is found that our estimate of DREA is largely insensitive to model choice. Radiative transfer calculations show that DREA at top‐of‐atmosphere is −4.6 ± 1.5 W/m2 for cloud‐free and −2.1 ± 0.7 W/m2 for all‐sky conditions, during year 2006. These fluxes are consistent with, albeit generally less negative over ocean than, former assessments. Unlike previous studies, our estimate is constrained by retrievals of global coverage SSA, which may justify different DREA values. Remarkable consistency is found in comparison with DREA based on CERES (Clouds and the Earth's Radiant Energy System) and MODIS observations.
Robustification of the sparse K-means clustering algorithm

Searching a dataset for the ‘‘natural grouping / clustering’’ is an important explanatory technique for understanding complex multivariate datasets. One might expect that the true underlying clusters present in a dataset differ only with respect to a small fraction of the features. Furthermore, one might afraid that the dataset might contain potential outliers. Through simulation studies, we find that an existing sparse clustering method can be severely affected by a single outlier. In this thesis, we develop a robust clustering method that is also able to perform variable selection: we robustified sparse K-means (Witten and Tibshirani [28]), based on the idea of trimmed Kmeans introduced by Gordaliza [7] and Gordaliza [8]. Since high dimensional datasets often contain quite a few missing observations, we made our proposed method capable of handling datasets with missing values. The performance of the proposed robust sparse K-means is assessed in various simulation studies and two data analyses. The simulation studies show that robust sparse K-means performs better than other competing algorithms in terms of both the selection of features and the selection of a partition when datasets are contaminated. The analysis of a microarray dataset shows that robust sparse K-means best reflects the oestrogen receptor status of the patients among all other competing algorithms. We also adapt Clest (Duboit and Fridlyand [5]) to our robust sparse K-means to provide an automatic robust procedure of selecting the number of clusters. Our proposed methods are implemented in the R package RSKC. Revision: ubcdiss.cls r28
European Journal of Human Resource Management Studies

The core of HRM is said to be the policies, rules and regulations that sets the tone for employees in regards to their behaviours at work. Positive, good, transparent, clear and non-discriminatory policies, rules and regulations at work should theoretically help employees in achieving and realizing their best potentials and in turn affect the organizations’ performance. This study examines the relationships between management of human resource policies and employees’ engagement (EE), and turnover intention (TI) in Brunei’s Islamic banks. Specifically, this study investigates the relationships of human resource policies (number of policies, the clarity of the policies, enforcement practices of the policies) on EE and TI. A total of 250 questionnaires were distributed to Islamic Banks in Brunei and 119 questionnaires were completed and returned by the respondents and used as the primary data in the analysis. The findings revealed that the management of human resource policies in Islamic banks in Brunei in overall is significantly positively related to employee engagement. On the contrary, management of human resource policy in overall is not significantly related to turnover intention. Keyword: human resource policy, employee engagement, turnover intention, human resource management, Islamic banking, Brunei Darussalam Salahudin Shahrul Nizam, Abdullah Muhammad Madi, Abdul Rani Nasir, Ali Nabilah MANAGEMENT OF HUMAN RESOURCE POLICIES AND ITS RELATIONSHIPS WITH EMPLOYEES’ ENGAGEMENT AND TURNOVER INTENTION IN BRUNEI’S ISLAMIC BANKS European Journal of Human Resource Management Studies Volume 1 │ Issue 2 │ 2018 79
New results for the analysis of linear systems with time‐invariant delays

This paper presents a comparison system approach for the analysis of stability and ℋ︁∞ performance of linear time‐invariant systems with unknown delays. The comparison system is developed by replacing the delay elements with certain parameter‐dependent Padé approximations. It is shown using the special properties of the Padé approximation to e−s that the value sets of these approximations provide outer and inner coverings for that of each delay element and that the robust stability of the outer covering system is a sufficient condition for the stability of the original time delay system. The inner covering system, in turn, is used to provide an upper bound on the degree of conservatism of the delay margin established by the sufficient condition. This upper bound is dependent only upon the Padé approximation order and may be made arbitrarily small. In the single delay case, the delay margin can be calculated explicitly without incurring any additional conservatism. In the general case, this condition can be reduced with some (typically small) conservatism to finite‐dimensional LMIs. Finally, this approach is also extended to the analysis of ℋ︁∞ performance for linear time‐delay systems with an exogenous disturbance. Copyright © 2003 John Wiley & Sons, Ltd.
Soft Actuating Sit-to-Stand Trainer Seat

In this paper, we propose a novel type of soft robot for sit to stand (STS) training, which is made with soft bellow actuators. Analysis with five healthy human subjects revealed that there is a statistically significant decrease in the peak and mean muscle activation signal from three out of the four groups of lower limb muscle for STS transition, namely, tibialis anterior, hamstrings, and quadriceps. The peak muscle activation decreased most drastically on the quadriceps muscle group (0.726 ± 0.467 to 0.269 ± 0.334). As reduced muscle activation signal correlates to less muscular effort required by the users, the results show the effectiveness of the device in partially supporting the STS transition of the subject, which subsequently serves as an STS trainer device.
Detecting clusters in moderate-to-high dimensional data: subspace clustering, pattern-based clustering, and correlation clustering

As a prolific research area in data mining, subspace clustering and related problems induced a vast amount of proposed solutions. However, many publications compare a new proposition -- if at all -- with one or two competitors or even with a so called "naive" ad hoc solution but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this tutorial, we try to clarify (i) the different problem definitions related to subspace clustering in general, (ii) the specific difficulties encountered in this field of research, (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches, and (iv) how several prominent solutions essentially tackle different problems.
Exploring undergraduates’ conceptions of elasticity, within a plate tectonics context, before and after experience with rock’s elastic behavior

Abstract The elastic rebound theory is a fundamental explanatory geoscience construct introduced in most introductory undergraduate geoscience courses. Classroom experience, supported by a recent case study of undergraduate students’ model-building activities, indicates that learning this theory tends to be incomplete, in spite of instruction employing external representations and other analogies. Using conceptual metaphor theory as a lens to investigate such instruction, this project sought to document students’ understandings of elasticity within a plate tectonics context, and identify and describe learning demands that must first be addressed for analogies and models to be effective. The project also evaluated the efficacy of an instructional intervention that provides learners with an experience with rocks that most daily encounters do not: the elastic deformation of rocks. Using a nonequivalent groups design of 81 undergraduate students enrolled in a 100-level geoscience course, this work revealed that prior to instruction all participants were familiar with the concept of elasticity. However, only 35.8% were able to demonstrate an understanding aligned with the scientific consensus. Relevant to the elastic rebound theory, just over 50% conceptualized tectonic plates and Earth’s upper interior as being composed of rocks, and 67.9% identified tectonic plates as elastic. However, indicative of a shallow understanding, only 18.5% recognized that rocks, which make up tectonic plates, behave elastically. The instructional intervention that allowed students to bend rocks resulted in a statistically significant increase in the number of students who conceptualized rocks as well as other solids as elastic on the posttest.
Exploring the space of perfectly balanced rhythms and scales

Periodic scales and meters typically embody “organizational principles” – their pitches and onset times are not randomly distributed, but structured by rules or constraints. Identifying such principles is useful for understanding existing music and for generating novel music. In this article, we identify and discuss a novel organizational principle for scales and rhythms that we feel is of both theoretical interest and practical utility: perfect balance. When distributed around the circle, perfectly balanced rhythms and scales have their “centre of gravity” at the centre of the circle. The present article serves as a repository of the theorems and definitions crucial to perfect balance. It also further explores its mathematical ramifications by linking the existing theorems to algebraic number theory and computational optimizations. In the Online Supplement, http://www.dynamictonality. com/perfect_balance_files/, we provide audio samples of perfectly balanced rhythmic loops and microtonal scales, computational routines, and video demonstrations of some of the concepts.
Monitoring long-term forest dynamics with scarce data: a multi-date classification implementation in the Ecuadorian Amazon

ABSTRACT Monitoring long-term forest dynamics is essential for assessing human-induced land-cover changes, and related studies are often based on the multi-decadal Landsat archive. However, in areas such as the Tropical Andes, scarce data and the resulting poor signal-to-noise ratio in time series data render the implementation of automated time-series analysis algorithms difficult. The aim of this research was to investigate a novel approach that combines image compositing, multi-sensor data fusion, and postclassification change detection that is applicable in data-scarce regions of the Tropical Andes, exemplified for a case study in Ecuador. We derived biennial deforestation and reforestation patterns for the period from 1992 to 2014, achieving accuracies of 82 ± 3% for deforestation and 71 ± 3% for reforestation mapping. Our research demonstrated that an adapted methodology allowed us to derive the forest dynamics from the Landsat time series, despite the abundant regional data gaps in the archive, namely across the Tropical Andes. This study, therefore, presented a novel methodology in support of monitoring long-term forest dynamics in areas with limited historical data availability.
Towards an Automatic Turing Test : Learning to Evaluate Dialogue

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itdr: An R package of Integral Transformation Methods to Estimate the SDR Subspaces in Regression

Sufficient dimension reduction (SDR) is an effective tool for regression models, offering a viable approach to address and analyze the nonlinear nature of regression problems. This paper introduces the itdr R package, a comprehensive and user-friendly tool that introduces several functions based on integral transformation methods for estimating SDR subspaces. In particular, the itdr package incorporates two key methods, namely the Fourier method (FM) and the convolution method (CM). These methods allow for estimating the SDR subspaces, namely the central mean subspace (CMS) and the central subspace (CS), in cases where the response is univariate. Furthermore, the itdr package facilitates the recovery of the CMS through the iterative Hessian transformation (IHT) method for univariate responses. Additionally, it enables the recovery of the CS by employing various Fourier transformation strategies, such as the inverse dimension reduction method, the minimum discrepancy approach using Fourier transformation, and the Fourier transform sparse inverse regression approach, specifically designed for cases with multivariate responses. To demonstrate its capabilities, the itdr package is applied to five different datasets. Furthermore, this package is the pioneering implementation of integral transformation methods for estimating SDR subspaces, thus promising significant advancements in SDR research.
Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects Supplementary Material

Pascal3D+ [12] provides images with 3D annotations for 12 object categories. The images are selected from the training and validation set of PASCAL VOC 2012 [4] and ImageNet [3], with 2k to 4k images in the wild per category. An approximate 3D CAD model is provided for each object as well as its 3D orientation in the image. Following the protocol of [5, 8, 11], we use the ImageNet-trainval and Pascal-train images as training data, and the 2,113 non-occluded and non-truncated objects of the Pascal-val images as testing data. As in [11], we use the metric Acc π 6 , which measures the percentage of test samples having a pose prediction error smaller than 6 : ∆(Rpred,Rgt) = ‖ log(R T predRgt)‖F/ √ 2 < 6 .
Kernel regression residual signal-based improved intrinsic time-scale decomposition for mechanical fault detection

Correct identification of the faults in mechanical components is the key to monitoring health conditions of machines. However, the interference of low-frequency signals and environmental noises greatly restricts the performance of traditional fault detection methods. Considering that the signals of interest (SOI) are usually carried by high-frequency signals and the interference of low-frequency signals, a novel preprocessing technique called one-level kernel regression residual decomposition (KRRD) is presented. Combined with the improved intrinsic time-scale decomposition (IITD) and Hilbert envelope analysis technique, a novel mechanical fault detection method is proposed. The method combines KRRD to extract the high-frequency signals from the raw vibration signals to track the faulty information, IITD to further extract the SOI from the high-frequency signals by removing the noises, and Hilbert envelope analysis to demodulate the denoised signals to detect faults in the rolling element bearings and gears. In order to verify the performance of the proposed method, the simulated data and real experimental data collected from faulty bearings and gears are analyzed. Three commonly used methods, namely empirical mode decomposition (EMD), local mean decomposition (LMD) and variational mode decomposition (VMD) are introduced and comparisons with the three combinations (KRRD and EMD, KRRD and LMD, KRRD and VMD) are given. The analysis results indicate that the proposed method is superior to other methods for detecting faults in mechanical components.
OEPARTEMENT TOEGEPASTE ECONOMISCHE WETENSCHAPPEN

This paper empirically tests the effects of Anti-Dumping (AD) protection on the price-cost margin of fIrms. To this end, we use a rich panel data set of 1,666 EU producers that were involved in AD cases initiated in 1996. Our [mdings indicate that price-cost margins in most cases significantly increase in the period of protection compared to a period before protection. In industries where competition is very tough before protection, we fail to find an increase in price-cost margins, while in industries with positive markups before protection, trade policy raises market power between 3% points and 15 % points, depending on the sector. Our results are robust to alternative specifications and estimation techniques, controlling for unobservable fixed effects and potential endogeneity of the regressors. Our findings are also consistent with recent theoretical models that deal with the economic effects on price behaviour in response to AD protection.
OEPARTEMENT TOEGEPASTE ECONOMISCHE WETENSCHAPPEN

For discrete arithmetic Asian options the payoff depends on the price average of the underlying asset. Due to the dependence struc ture between the prices of the underlying asset, no simple exact pricing formula exists, not even in a Black-Scholes setting. In the recent lit erature, several approximations and bounds for the price of this type of option are proposed. One of these approximations consists of re placing the distribution of the stochastic price average by an ad hoc distribution (e.g. Lognormal or Inverse Gaussian) with the same first and second moment. In this paper we use a different approach and combine a lower and upper bound into a new analytical approxima tion. This approximation can be calculated efficiently, turns out to be very accurate and moreover, it has the correct first and second mo ment. Since the approximation is analytical, we can also calculate the corresponding hedging Greeks and construct a replicating strategy.
Geological modeling using a recursive convolutional neural networks approach

Resource models are constrained by the extent of geological units that often depend on the lithology, alteration and mineralization. A three dimensional model of these geological units must be built from scarce information coming from drillholes and limited understanding about the geological setting in which the ore deposit is places. In this work, we present a new technique for multiple-point geostatistical simulation based on a recursive convolutional neural network approach (RCNN). The method requires conditioning data and a training image that depicts the type of geological structures expected to be found in the deposit. This training image is used to learn the patterns of categories found and these are imposed in the final simulated model conditioned by the categories found during logging at the actual drill-hole samples. A lithological modeling process is carried out in a copper deposit in Chile to demonstrate the method. Comparison with current techniques and spatial metrics are used to clarify concepts and RCNN properties. Also, strengths and weaknesses of the methodology are discussed by briefly reviewing the theoretical perspective and looking into some of its practical aspects.
Investigation on the performances of a twin arm tensioning device

In the present work, an experimental analysis of the performances of a twin arm tensioner is conducted. The investigated device is used in an automotive belt drive system mounting a belt starter generator. This configuration represents the latest trend of micro-hybrid technologies and is devoted to keep the tension of the belt within a reasonable range, while obtaining the highest possible efficiency in both motor and generator modes. At first, the functionality of a twin arm tensioner is investigated with a static model. Afterward, the performances of a real tensioner are experimentally assessed through a dedicated test rig in quasi-static conditions. The system is benchmarked in terms of angular displacement of the tensioner arms, belt tensions on the corresponding spans, and sliding arc in different operating conditions. Finally, experimental and simulation results are compared. It is shown that the proposed static model is able to capture the behavior of the real device and highlight its functionality.
The Influence of Employer Branding Dimensions on Generation Z Women’s Intention to Apply For a Job with Person-Organization Fit as Mediating Variable (A Study of Bandung Institute of Technology)

An organization's innovation potential is highly correlated with its ability to use its intellectual and organizational knowledge resources. Consequently, hiring high-potential individuals is one of the key issues for businesses looking to maintain a competitive advantage in the sector. However, the workforce is experiencing a demographic shift as Generation Z enters the job market in significant numbers. Given that Generation Z will ultimately dominate the workforce, this demographic shift poses a long-term challenge for businesses to understand their demands, preferences, and factors impacting their interest in applying for jobs. Additionally, establishing gender equality and increasing the involvement of women in the workforce are top recruitment goals for businesses today. In light of these circumstances, this study examines how different aspects of employer branding influence Generation Z women's intention to apply for a job and whether the person-organization fit plays a mediating role. The research employs a quantitative approach, distributing questionnaires to 335 female students of Generation Z at Bandung Institute of Technology, which will be analyzed using PLS-SEM (Partial Least Squares Structural Equation Modeling). The findings demonstrate that within the context of Generation Z at Bandung Institute of Technology, four of the five employer branding dimensions—development, economic, application, and interest value—significantly and positively impact the intention to apply for a job. However, the study also indicates that person-organization fit does not act as a mediating variable in this regard. This research offers valuable insights to companies, allowing them to improve their existing employer branding strategies and enhance their recruiting and talent acquisition approaches. By effectively attracting, engaging, and retaining Generation Z women in the workforce, companies can foster greater gender diversity and equality in the present job market.