Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Kompetensi, Kompensasi dan Kepemimpinan Terhadap Kinerja Karyawan

Successful companies get contributions from the performance of their employees. Employees are the most important and foremost asset in a company. With good and quality employee performance, this will help the company to achieve its goals. Three things affect employee performance, namely competence, compensation and leadership. Competence is the main requirement to do work assignments. Compensation is the level of benefits received by employees. Excellent leadership quality to lead a company, by finding employees who are competent in the field of work that has been determined and provide adequate compensation in stages for employees who excel, this will improve the performance of company employees. The technique used in determining the sample is purposive sampling. The number of samples used was 35 people from the total population. The results showed that, [1] competence had a significant effect on job performance; [2] compensation has a significant effect on job performance; [3] leadership has a significant effect on job performance
Gender Classification and Bias Mitigation in Facial Images

Gender classification algorithms have important applications in many domains today such as demographic research, law enforcement, as well as human-computer interaction. Recent research showed that algorithms trained on biased benchmark databases could result in algorithmic bias. However, to date, little research has been carried out on gender classification algorithms’ bias towards gender minorities subgroups, such as the LGBTQ and the non-binary population, who have distinct characteristics in gender expression. In this paper, we began by conducting surveys on existing benchmark databases for facial recognition and gender classification tasks. We discovered that the current benchmark databases lack representation of gender minority subgroups. We worked on extending the current binary gender classifier to include a non-binary gender class. We did that by assembling two new facial image databases: 1) a racially balanced inclusive database with a subset of LGBTQ population 2) an inclusive-gender database that consists of people with non-binary gender. We worked to increase classification accuracy and mitigate algorithmic biases on our baseline model trained on the augmented benchmark database. Our ensemble model has achieved an overall accuracy score of 90.39%, which is a 38.72% increase from the baseline binary gender classifier trained on Adience. While this is an initial attempt towards mitigating bias in gender classification, more work is needed in modeling gender as a continuum by assembling more inclusive databases.
On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere

Understanding the identities, needs, realities and development of subcultures has been a long term target of sociology and cultural studies. Socio-cultural linguistics, in particular, examines the use of language and, in particular, the existence and use of neologisms, slang and jargon. These terms capture concepts and expressions that are not in common use and represent the new realities, norms and values of subcommunities. Identifying and understanding such terms, however, is a very complex task, particularly considering the vast amount of content that is currently available online for many such groups. In this paper, we propose a combination of computational and socio-linguistic methods to automatically extract new terminology from large amounts of data, using word-embeddings to semantically contextualise their meaning. As a use case, we explore subculture on the platform Reddit. More specifically, we investigate groups considered part of the manosphere, a loose online community where men’s perspectives, gripes, frustrations and desires are explicitly expressed and where women are typically targets of hostility. Characterisations of this group as a subculture are then provided, based on an in-depth analysis of the identified jargon.
Intrinsic Flat Stability of Manifolds with Boundary where Volume Converges and Distance is Bounded Below

Given a compact, connected, and oriented manifold with boundary $M$ and a sequence of smooth Riemannian metrics defined on it, $g_j$, we prove volume preserving intrinsic flat convergence of the sequence to the smooth Riemannian metric $g_0$ provided $g_j$ always measures vectors strictly larger than or equal to $g_0$, the diameter of $g_j$ is uniformly bounded, the volume of $g_j$ converges to the volume of $g_0$, and $L^{\frac{m-1}{2}}$ convergence of the metrics restricted to the boundary. Many examples are reviewed which justify and explain the intuition behind these hypotheses. These examples also show that uniform, Lipschitz, and Gromov-Hausdorff convergence are not appropriate in this setting. Our results provide a new rigorous method of proving some special cases of the intrinsic flat stability of the positive mass theorem.
RASSINE: Interactive tool for normalising stellar spectra

Aims. We provide an open-source code allowing an easy, intuitive, and robust normalisation of spectra. Methods. We developed RASSINE, a Python code for normalising merged 1D spectra through the concepts of convex hulls. The code uses six parameters that can be easily fine-tuned. The code also provides a complete user-friendly interactive interface, including graphical feedback, that helps the user to choose the parameters as easily as possible. To facilitate the normalisation even further, RASSINE can provide a first guess for the parameters that are derived directly from the merged 1D spectrum based on previously performed calibrations. Results. For HARPS spectra of the Sun that were obtained with the HELIOS solar telescope, a continuum accuracy of 0.20% on line depth can be reached after normalisation with RASSINE. This is three times better than with the commonly used method of polynomial fitting. For HARPS spectra of α Cen B, a continuum accuracy of 2.0% is reached. This rather poor accuracy is mainly due to molecular band absorption and the high density of spectral lines in the bluest part of the merged 1D spectrum. When wavelengths shorter than 4500 Å are excluded, the continuum accuracy improves by up to 1.2%. The line-depth precision on individual spectrum normalisation is estimated to be ∼0.15%, which can be reduced to the photon-noise limit (0.10%) when a time series of spectra is given as input for RASSINE. Conclusions. With a continuum accuracy higher than the polynomial fitting method and a line-depth precision compatible with photon noise, RASSINE is a tool that can find applications in numerous cases, for example stellar parameter determination, transmission spectroscopy of exoplanet atmospheres, or activity-sensitive line detection.
Entanglement of Pseudo-Hermitian Random States

In a recent paper (A. Fring and T. Frith, Phys. Rev A 100, 101102 (2019)), a Dyson scheme to deal with density matrix of non-Hermitian Hamiltonians has been used to investigate the entanglement of states of a PT-symmetric bosonic system. They found that von Neumann entropy can show a different behavior in the broken and unbroken regime. We show that their results can be recast in terms of an abstract model of pseudo-Hermitian random matrices. It is found however that although the formalism is practically the same, the entanglement is not of Fock states but of Bell states.
The $L^p$ Dirichlet and Regularity problems for second order Elliptic Systems with application to the Lam\'e system

In the paper [11] we have introduced new solvability methods for strongly elliptic second order systems in divergence form on a domains above a Lipschitz graph, satisfying L-boundary data for p near 2. The main novel aspect of our result is that it applies to operators with coefficients of limited regularity and applies to operators satisfying a natural Carleson condition that has been first considered in the scalar case. In this paper we extend this result in several directions. We improve the range of solvability of the L Dirichlet problem to the interval 2 − ε < p < 2(n−1) (n−3) + ε, for systems in dimension n = 2, 3 in the range 2− ε < p < ∞. We do this by considering solvability of the Regularity problem (with boundary data having one derivative in L) in the range 2− ε < p < 2 + ε. Secondly, we look at perturbation type-results where we can deduce solvability of the L Dirichlet problem for one operator from known L Dirichlet solvability of a “close” operator (in the sense of Carleson measure). This leads to improvement of the main result of the paper [11]; we establish solvability of the L Dirichlet problem in the interval 2− ε < p < 2(n−1) (n−2) + ε under a much weaker (oscillation-type) Carleson condition. A particular example of the system where all these results apply is the Lamé operator for isotropic inhomogeneous materials with Poisson ratio ν < 0.396. In this specific case further improvements of the solvability range are possible, see [12].
Axion-mediated forces and CP violation in left-right models

We compute the CP-violating (CPV) scalar axion coupling to nucleons in the framework of baryon chiral perturbation theory and we apply the results to a class of left-right symmetric models. The correlated constraints with other CPV observables show that the predicted axion nucleon coupling is within the reach of axion-mediated force experiments as ARIADNE for $M_{W_R}$ up to $1000$ TeV.
Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a recurrent neural network to simultaneously extract both spatial and temporal features from under-sampled, motion-blurred cine cardiac images for improved image quality. The experimental results demonstrate substantially improved image quality on two clinical test datasets. Also, our method enables data-driven frame interpolation at an enhanced temporal resolution. Compared with existing methods, our deep learning approach gives a superior performance in terms of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR).
GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2 – 24.3 M e black hole and a compact object with a mass of 2.50 – 2.67 M e ( all measurements quoted at the 90% credible level ) . The gravitational-wave signal, GW190814, was observed during LIGO ’ s and Virgo ’ s third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to 18.5 deg 2 at a distance of -+ 241 4541 Mpc; no electromagnetic counterpart has been con ﬁ rmed to date. The source has the most unequal mass ratio yet measured with gravitational waves, -+ 0.112 0.0090. 008 , and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to  0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is con ﬁ rmed at high con ﬁ dence. We estimate a merger rate density of 1 – 23 Gpc − 3 yr − 1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.
Mechanisms by which conjugated linoleic acid causes human adipocyte delipidation

"Obesity is an important health issue, having risen to epidemic proportions in the U.S. Use of conjugated linoleic acid (CLA), positional and geometric isomers of linoleic acid, has received recent attention due to its potential health benefits including the reduction of fat mass in animals. However, the effectiveness and safety of CLA consumption in humans remains unclear. Our group previously reported that trans-10, cis-12 CLA impaired the conversion of preadipocytes into lipid-filled adipocytes (e.g., differentiation) and caused adipocyte delipidation that involved inflammatory cytokines in a human cell model. However, the isomer-specific mechanism for these events was unknown. Thus, this research examined mechanisms by which trans-10, cis-12 CLA induced adipocyte delipidation, inflammation, and insulin resistance in primary cultures of human adipocytes. Delipidation of adipocytes by trans-10, cis-12 CLA was accompanied by increased lipolysis and changes in the morphology of lipid droplets and the expression and localization of proteins regulating lipid droplet metabolism. This process involved the translational control of adipose differentiated related protein (ADRP) through activation of mTOR/p70S6K/S6 signaling and transcriptional control of perilipin A. Prior to these morphological changes, it was shown that trans-10, cis-12 CLA promoted nuclear factor κB (NFκB) and mitogen activated protein kinase (MAPK) activation and subsequent induction of interleukin (IL)-6 which were, at least in part, responsible for trans-10, cis-12 CLA-mediated suppression of peroxisome proliferator activated receptor gamma (PPAR)γ target gene expression and insulin sensitivity in human adipocytes. The essential role of NFκB on CLA-induced inflammation was confirmed by using RNA interference. Further studies were conducted examining the localization and characterization of the inflammatory response, including the type of cells involved, using lipopolysaccharide (LPS) as the inflammatory agent. It was demonstrated that LPS-induced, NFκB-dependent proinflammatory cytokine expression was predominantly from preadipocytes, which led to, at least in part, the suppression of PPAR activity and adipogenic gene expression and insulin sensitivity. Collectively, these data support the emerging concept that adipose tissue is a dynamic endocrine organ with the capacity to generate inflammatory signals that impact glucose and lipid metabolism. Furthermore, human preadipocytes have the capacity to generate these inflammatory signals induced by trans-10, cis-12 CLA and LPS, subsequently causing insulin resistance in neighboring adipocytes. These studies also revealed that NFκB- and MAPK-signaling mediate inflammation and insulin resistance induced by CLA and LPS. Thus, although the trans-10, cis-12 isomer of CLA may decrease the size and lipid content of human adipocytes, it may also cause insulin resistance, which is a hallmark of type 2 diabetes. "--Abstract from author supplied metadata.
Approximate Cross-Validation for Structured Models

Many modern data analyses benefit from explicitly modeling dependence structure in data -- such as measurements across time or space, ordered words in a sentence, or genes in a genome. Cross-validation is the gold standard to evaluate these analyses but can be prohibitively slow due to the need to re-run already-expensive learning algorithms many times. Previous work has shown approximate cross-validation (ACV) methods provide a fast and provably accurate alternative in the setting of empirical risk minimization. But this existing ACV work is restricted to simpler models by the assumptions that (i) data are independent and (ii) an exact initial model fit is available. In structured data analyses, (i) is always untrue, and (ii) is often untrue. In the present work, we address (i) by extending ACV to models with dependence structure. To address (ii), we verify -- both theoretically and empirically -- that ACV quality deteriorates smoothly with noise in the initial fit. We demonstrate the accuracy and computational benefits of our proposed methods on a diverse set of real-world applications.
Optimal control of a malaria model with long-lasting insecticide-treated nets

A deterministic multi-stage malaria model with a non-therapeutic control measure and the effect of loss of immunity due to the use of the Long-Lasting bednets with a control perspective is formulated and analyzed both theoretically and numerically. The model basic reproduction number is derived, and analytical results show that the model's equilibria are locally and globally asymptotically stable when certain threshold conditions are satisfied. Pontryagin's Maximum Principle with respect to a time dependent constant is used to derive the necessary conditions for the optimal usage of the Long-Lasting Insecticide-treated bednets (LLINs) to mitigate the malaria transmission dynamics. This is accomplished by introducing biologically admissible controls and $ \epsilon\% $-approximate sub-optimal controls. Forward-backward fourth-order Runge-Kutta method is used to numerically solve the optimal control problem. We observe that the disadvantage (loss of immunity, even at its maximum) in the use of bednets is compensated by the benefit of the number of susceptible/infected individuals excluded from the malaria disease dynamics, the only danger being the poor use of the long-lasting bednets. Moreover, it is possible to get closer to the optimal results with a realistic strategy. The results from this study could help public health planners and policy decision-makers to design reachable and more practical malaria prevention programs "close" to the optimal strategy.
A Velocity-Entropy Invariance theorem for the Chapman-Jouguet detonation

The velocity and the specific entropy of the Chapman-Jouguet (CJ) equilibrium detonation in a homogeneous explosive are shown to be invariant under the same variations of the initial pressure and temperature. The CJ state and isentrope can then be defined from the CJ velocity or, conversely, the CJ velocity from one CJ variable, without equation of state for detonation products. For gaseous explosives, comparison to calculations with detailed chemical equilibrium shows agreement to within O(0.1)%. However, the CJ pressures of four carbonate liquid explosives are found about 20 % larger than measured values: the CJ-equilibrium model appears not to apply to condensed carbonate explosives, which supports a former conclusion by Davis, Craig and Ramsay, although for the opposite reason. A simple hydrodynamic criterion for assessing the representativeness of this CJ model is thus proposed, which nevertheless cannot determine which of its assumptions may not be satisfied, namely CJ equilibrium, single-phase fluid, and laminar flow. This invariance might be an illustration of a general feature of hyperbolic systems and their characteristic surfaces.
Effects of treatment with ethanol extract of Gardenia sokotensis on haematological and biochemical changes in Trypanosoma brucei brucei infected rabbits

This study was carried out to determine the comparative effects of 7-day treatment with the ethanolic crude root extract of Gardenia sokotensis (administered orally at dose rate of 60 mg/kg body weight) and diminazen aceturate (Berenil, administered once, intramuscularly, at the dose of 3.5 mg/kg body weight) on Trypanosoma brucei brucei infection-induced changes in serum biochemical and haematological parameters in rabbits. The treatments with the two forms of drugs were commenced when parasitaemia was at its peak level on day 41 post-infection. Significant reduction in the level of parasitaemia was observed following the 7-day treatment of the T. brucei brucei infected group of rabbits with the root extract of G. sokotensis. Concomitantly, amelioration of the anaemia, as reflected by improvement in the packed cell volume, and some of the serum biochemical changes was observed. These latter changes were however not as marked as those observed in the Berenil-treated group. The findings in this study strongly suggest that the root extract of G. sokotensis may contain some active substance(s) that exhibit anti-trypanosomal properties. Key words: Gardenia sokotensis, crude root extract, haematological and biochemical changes.
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these models. Various notions and measures of fairness have been proposed to ensure that a decision-making system does not disproportionately harm (or benefit) particular subgroups of the population. In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks. We argue that in some cases, it may be easier for an attacker to target a particular subgroup, resulting in a form of robustness bias. We show that measuring robustness bias is a challenging task for DNNs and propose two methods to measure this form of bias. We then conduct an empirical study on state-of-the-art neural networks on commonly used real-world datasets such as CIFAR-10, CIFAR-100, Adience, and UTKFace and show that in almost all cases there are subgroups (in some cases based on sensitive attributes like race, gender, etc) which are less robust and are thus at a disadvantage. We argue that this kind of bias arises due to both the data distribution and the highly complex nature of the learned decision boundary in the case of DNNs, thus making mitigation of such biases a non-trivial task. Our results show that robustness bias is an important criterion to consider while auditing real-world systems that rely on DNNs for decision making. Code to reproduce all our results can be found here: https://github.com/nvedant07/Fairness-Through-Robustness