Miracle-dz/UniFAR-FTUs huggingface dataset:
On the relationship between total quality management practices and firm performance in Vietnam: The mediating role of non-financial performance

Article history: Received: November 27, 2019 Received in revised format: November 29 2019 Accepted: January 7, 2020 Available online: January 8, 2020 Total quality management (TQM) is one of the popular management methods in many developed countries, while it is an entirely new method for emerging economies. Although TQM practices contribute to the sustainable development of organizations, it is challenging to adopt this method efficiently. Six criteria factors are playing important roles in TQM implementation, which are: leadership, process management, quality information, employee training, customer focus, and supplier management. Each factor has different roles for firm performance represented by financial performance and non – financial performance. Identifying the relationship between TQM practices and the performance in Vietnamese enterprises, the research investigates the role of non – financial performance as a mediator variable in the relationship between TQM practices and financial performance. Through designing a questionnaire to conduct a survey, this paper studies the mediating role of non – financial performance on the relationship between TQM practices and financial performance utilizing SPSS 20 and AMOS 22 software with 211 Vietnamese enterprises by using SEM analysis. The estimation results propose that non – financial performance plays a vital role as a full mediator in the relationship between TQM practices and financial performance in the Vietnam context. In this research, there is no difference in the mediating effect level of non – financial performance between TQM companies & non – TQM companies as well as ISO adopters & non – ISO adopters. The study suggests that Vietnamese enterprises should have strict commitment to place TQM philosophy in actions at this early step of TQM implementation process to achieve considerable benefits in long-term development. © 2020 by the authors; licensee Growing Science, Canada
Taking X-ray Diffraction to the Limit: Macromolecular Structures from Femtosecond X-ray Pulses and Diffraction Microscopy of Cells with Synchrotron Radiation

The methodology of X-ray crystallography has recently been successfully extended to the structure determination of non-crystalline specimens. The phase problem was solved by using the oversampling method, which takes advantage of “continuous” diffraction pattern from non-crystalline specimens. Here we review the principle of this newly developed technique and discuss the ongoing experiments of imaging non-periodic objects, like cells and cellular structures using coherent and bright X-rays from the 3 generation synchrotron radiation. In the longer run, the technique may be applied to image single biomolecules by using the anticipated X-ray free electron lasers. Computer simulations have so far demonstrated two important steps: (i) by using an extremely intense femtosecond X-ray pulse, a diffraction pattern can be recorded from a macromolecule before radiation damage manifests itself, and (ii) the phase information can be ab initio retrieved from a set of calculated noisy diffraction patterns of single protein molecules. CONTENTS PERSPECTIVE AND OVERVIEW THE OVERSAMPLING PHASING METHOD AND ITERATIVE ALGORITHMS The Principle of the Oversampling Method Iterative Algorithms EXPERIMENTS USING SYNCHROTRON RADIATION FROM STORAGE-RING-BASED TO LINAC-BASED X-RAY SOURCES OVERCOMING THE RADIATION BARRIER USING FEMTOSECOND XRAY PULSES POTENTIAL OF IMAGING SINGLE PROTEIN MOLECULES SUMMARY AND OUTLOOK
Adsorption Behavior of the Hydroxyl Radical and Its Effects on Monolayer MoS2

Based on first-principles density functional theory calculations, we investigated a modified routine using hydroxyl adsorption that recently demonstrated the controlled growth of MoS2 monolayers. The new growth approach impedes the deposition of a second MoS2 layer; however, the hydroxyl adsorption and its effects have been mostly unexplored. Through this study, we first explored the adsorption behaviors of the hydroxyl radical (OH) on monolayer MoS2 and briefly discussed its effects on the stability and electronic structure. Monolayer MoS2 repels charged OH–, whereas the adsorption of the neutral OH radical is energetically favorable; the corresponding adsorption energies are 0.09 eV and −1.35 eV, respectively. The diffusion barrier of the OH radical on MoS2 is 0.52 eV, indicating that the molecule can quickly diffuse. Next, the study demonstrated that for multiple OH adsorptions, a concerted reaction including OH dissociation and H2O formation is more energetically favorable than the adsorption of two OH molecules by 2.50 eV, which in turn results in a mixed adsorption configuration of O and OH. In addition, we revealed that the OH adsorption creates a mid-gap state and facilitates the reconstruction of the MoS2 edge.
Adversarially Robust Frame Sampling with Bounded Irregularities

In recent years, video analysis tools for automatically extracting meaningful information from videos are widely studied and deployed. Because most of them use deep neural networks which are computationally expensive, feeding only a subset of video frames into such algorithms is desired. Sampling the frames with fixed rate is always attractive for its simplicity, representativeness, and interpretability. For example, a popular cloud video API generated video and shot labels by processing only the first frame of every second in a video. However, one can easily attack such strategies by placing chosen frames at the sampled locations. In this paper, we present an elegant solution to this sampling problem that is provably robust against adversarial attacks and introduces bounded irregularities as well.
Path-Based Contextualization of Knowledge Graphs for Textual Entailment.

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph. The task in the case of this paper is the textual entailment problem, and the context is a relevant sub-graph for an instance of the textual entailment problem -- where given two sentences p and h, the entailment relationship between them has to be predicted automatically. We base our methodology on finding paths in a cost-customized external knowledge graph, and building the most relevant sub-graph that connects p and h. We show that our path selection mechanism to generate sub-graphs not only reduces noise, but also retrieves meaningful information from large knowledge graphs. Our evaluation shows that using information on entities as well as the relationships between them improves on the performance of purely text-based systems.
Novel Rhenium Carbides at 200 GPa

Laser heating of rhenium in a diamond anvil cell to 3000 K at about 200 GPa results in formation of two previously unknown rhenium carbides, hexagonal WC-type structured ReC and orthorhombic TiSi2-type structured ReC2. The Re-C slid solution formed at multimegabar pressure has the carbon content of ca. 20 at%. Unexpectedly long C-C distances (ca. 1.76-1.85 A) in 'graphene-like' carbon nets in the structure of ReC2 cannot be explained by a simple covalent bonding between carbon atoms and suggest that at very high pressures the mechanism of interaction between carbon atoms in inorganic compounds may be different from that considered so far.
Efficient, Noise-Tolerant, and Private Learning via Boosting

We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-tolerant PAC learners. To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension. We give two sample complexity bounds for our large-margin halfspace learner. One bound is based only on differential privacy, and uses this guarantee as an asset for ensuring generalization. This first bound illustrates a general methodology for obtaining PAC learners from privacy, which may be of independent interest. The second bound uses standard techniques from the theory of large-margin classification (the fat-shattering dimension) to match the best known sample complexity for differentially private learning of large-margin halfspaces, while additionally tolerating random label noise.
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations

Abstract. Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification. As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.
Selfish Robustness and Equilibria in Multi-Player Bandits

Motivated by cognitive radios, stochastic multi-player multi-armed bandits gained a lot of interest recently. In this class of problems, several players simultaneously pull arms and encounter a collision - with 0 reward - if some of them pull the same arm at the same time. While the cooperative case where players maximize the collective reward (obediently following some fixed protocol) has been mostly considered, robustness to malicious players is a crucial and challenging concern. Existing approaches consider only the case of adversarial jammers whose objective is to blindly minimize the collective reward. We shall consider instead the more natural class of selfish players whose incentives are to maximize their individual rewards, potentially at the expense of the social welfare. We provide the first algorithm robust to selfish players (a.k.a. Nash equilibrium) with a logarithmic regret, when the arm performance is observed. When collisions are also observed, Grim Trigger type of strategies enable some implicit communication-based algorithms and we construct robust algorithms in two different settings: the homogeneous (with a regret comparable to the centralized optimal one) and heterogeneous cases (for an adapted and relevant notion of regret). We also provide impossibility results when only the reward is observed or when arm means vary arbitrarily among players.
Combined search for anisotropic birefringence in the gravitational-wave transient catalog GWTC-1

The discovery of gravitational waves (GWs) provides an unprecedented arena to test general relativity, including the gravitational Lorentz invariance violation (gLIV). In the propagation of GWs, a generic gLIV leads to anisotropy, dispersion, and birefringence. GW events constrain the anisotropic birefringence particularly well. Kostelecký and Mewes (2016) performed a preliminary analysis for GW150914. We improve their method and extend the analysis systematically to the whole GW transient catalog, GWTC-1. This is the first global analysis of the spacetime anisotropic Lorentzian structure with a catalog of GWs, where multiple events are crucial in breaking the degeneracy among gLIV parameters. With the absence of abnormal propagation, we obtain new limits on 34 coefficients for gLIV in the nonminimal gravity that surpass previous limits by ∼102–105.
On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods. Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PYTORCH called LUMIN is presented, which incorporates all of the techniques studied.
What do observations tell us about the highest-redshift supermassive black holes?

Abstract I review the current understanding of some key properties of the earliest growing supermassive black holes (SMBHs), as determined from the most up-to-date observations of z ≲ 5 quasars. This includes their accretion rates and growth history, their host galaxies, and the large-scale environments that enabled their emergence less than a billion years after the Big Bang. The available multi-wavelength data show that these SMBHs are consistent with Eddington-limited, radiatively efficient accretion that had to proceed almost continuously since very early epochs. ALMA observations of the hosts’ ISM reveal gas-rich, well developed galaxies, with a wide range of SFRs that may exceed ∼1000 Mȯyr−1. Moreover, ALMA uncovers a high fraction of companion, interacting galaxies, separated by < 100 kpc (projected). This supports the idea that the first generation of high-mass, luminous SMBHs grew in over-dense environments, and that major mergers may be important drivers for rapid SMBH and host galaxy growth. Current X-ray surveys cannot access the lower-mass, supposedly more abundant counterparts of these rare z ≳ 5 massive quasars, which should be able to elucidate the earliest stages of BH formation and growth. Such lower-mass nuclear BHs will be the prime targets of the deepest surveys planned for the next generation of facilities, such as the upcoming Athena mission and the future Lynx mission concept.
Multi-label Relation Modeling in Facial Action Units Detection

This paper describes an approach to the facial action units detections. The involved action units (AU) include AU1 (Inner Brow Raiser), AU2 (Outer Brow Raiser), AU4 (Brow Lowerer), AU6 (Cheek Raise), AU12 (Lip Corner Puller), AU15 (Lip Corner Depressor), AU20 (Lip Stretcher), and AU25 (Lip Part). Our work relies on the dataset released by the FG-2020 Competition: Affective Behavior Analysis In-the-Wild (ABAW). The proposed method consists of the data preprocessing, the feature extraction and the AU classification. The data preprocessing includes the detection of face texture and landmarks. The texture static and landmark dynamic features are extracted through neural networks and then fused as the feature latent representation. Finally, the fused feature is taken as the initial hidden state of a recurrent neural network with a trainable lookup AU table. The output of the RNN is the results of AU classification. The detected accuracy is evaluated with 0.5$\times$accuracy + 0.5$\times$F1. Our method achieve 0.56 with the validation data that is specified by the organization committee.
Exact out-of-equilibrium steady states in the semiclassical limit of the interacting Bose gas

We study the out-of-equilibrium properties of a classical integrable non-relativistic theory, with a time evolution initially prepared with a finite energy density in the thermodynamic limit. The theory considered here is the Non-Linear Schrödinger equation which describes the dynamics of the one-dimensional interacting Bose gas in the regime of high occupation numbers. The main emphasis is on the determination of the late-time Generalised Gibbs Ensemble (GGE), which can be efficiently semi-numerically computed on arbitrary initial states, completely solving the famous quench problem in the classical regime. We take advantage of known results in the quantum model and the semiclassical limit to achieve new exact results for the momenta of the density operator on arbitrary GGEs, which we successfully compare with ab-initio numerical simulations. Furthermore, we determine the whole probability distribution of the density operator (full counting statistics), whose exact expression is still out of reach in the quantum model.
Erratum: Can magnetized turbulence set the mass scale of stars?

Understanding the evolution of self-gravitating, isothermal, magnetized gas is crucial for star formation, as these physical processes have been postulated to set the initial mass function (IMF). We present a suite of isothermal magnetohydrodynamic (MHD) simulations using the gizmo code that follow the formation of individual stars in giant molecular clouds (GMCs), spanning a range of Mach numbers found in observed GMCs ($\mathcal {M} \sim 10\! -\! 50$). As in past works, the mean and median stellar masses are sensitive to numerical resolution, because they are sensitive to low-mass stars that contribute a vanishing fraction of the overall stellar mass. The mass-weighted median stellar mass M50 becomes insensitive to resolution once turbulent fragmentation is well resolved. Without imposing Larson-like scaling laws, our simulations find $M_\mathrm{50} \,\, \buildrel\propto \over \sim \,\,M_\mathrm{0} \mathcal {M}^{-3} \alpha _\mathrm{turb}\, \mathrm{SFE}^{1/3}$ for GMC mass M0, sonic Mach number $\mathcal {M}$, virial parameter αturb, and star formation efficiency SFE = M⋆/M0. This fit agrees well with previous IMF results from the ramses, orion2, and sphng codes. Although M50 has no significant dependence on the magnetic field strength at the cloud scale, MHD is necessary to prevent a fragmentation cascade that results in non-convergent stellar masses. For initial conditions and SFE similar to star-forming GMCs in our Galaxy, we predict M50 to be $\gt 20 \, \mathrm{M}_{\odot }$, an order of magnitude larger than observed ($\sim 2 \, \mathrm{M}_\odot$), together with an excess of brown dwarfs. Moreover, M50 is sensitive to initial cloud properties and evolves strongly in time within a given cloud, predicting much larger IMF variations than are observationally allowed. We conclude that physics beyond MHD turbulence and gravity are necessary ingredients for the IMF.
Semantic Search of Memes on Twitter

Memes are becoming a useful source of data for analyzing behavior on social media. However, a problem to tackle is how to correctly identify a meme. As the number of memes published every day on social media is huge, there is a need for automatic methods for classifying and searching in large meme datasets. This paper proposes and compares several methods for automatically classifying images as memes. Also, we propose a method that allows us to implement a system for retrieving memes from a dataset using a textual query. We experimentally evaluate the methods using a large dataset of memes collected from Twitter users in Chile, which was annotated by a group of experts. Though some of the evaluated methods are effective, there is still room for improvement.
Optimal Confidence Regions for the Multinomial Parameter

Construction of tight confidence regions and intervals is central to statistical inference and decision-making. Consider an empirical distribution $\widehat{\boldsymbol{p}}$ generated from $n$ iid realizations of a random variable that takes one of $k$ possible values according to an unknown distribution $\boldsymbol{p}$. This is analogous with a single draw from a multinomial distribution. A confidence region is a subset of the probability simplex that depends on $\widehat{\boldsymbol{p}}$ and contains the unknown $\boldsymbol{p}$ with a specified confidence. This paper shows how one can construct minimum average volume confidence regions, answering a long standing question. We also show the optimality of the regions directly translates to optimal confidence intervals of functionals, such as the mean, variance and median.
Bilinear Fractional Integral Operators

We study the bilinear fractional integral considered by Kenig and Stein, where linear combinations of variables with matrix coefficients are involved. Under more general settings, we give a complete characterization of the corresponding parameters for which the bilinear fractional integral is bounded from $L^{p_1}(\mathbb R^{n_1}) \times L^{p_2}(\mathbb R^{n_2})$ to $L^q(\mathbb R^m)$.
Bootstrapping a DQN Replay Memory with Synthetic Experiences

An important component of many Deep Reinforcement Learning algorithms is the Experience Replay which serves as a storage mechanism or memory of made experiences. These experiences are used for training and help the agent to stably find the perfect trajectory through the problem space. The classic Experience Replay however makes only use of the experiences it actually made, but the stored samples bear great potential in form of knowledge about the problem that can be extracted. We present an algorithm that creates synthetic experiences in a nondeterministic discrete environment to assist the learner. The Interpolated Experience Replay is evaluated on the FrozenLake environment and we show that it can support the agent to learn faster and even better than the classic version.
Generalized Structure Preserving Preconditioners for Frame-Based Image Deblurring

We are interested in fast and stable iterative regularization methods for image deblurring problems with space invariant blur. The associated coefficient matrix has a Block Toeplitz Toeplitz Blocks (BTTB) like structure plus a small rank correction depending on the boundary conditions imposed on the imaging model. In the literature, several strategies have been proposed in the attempt to define proper preconditioner for iterative regularization methods that involve such linear systems. Usually, the preconditioner is chosen to be a Block Circulant with Circulant Blocks (BCCB) matrix because it can efficiently exploit Fast Fourier Transform (FFT) for any computation, including the (pseudo-)inversion. Nevertheless, for ill-conditioned problems, it is well known that BCCB preconditioners cannot provide a strong clustering of the eigenvalues. Moreover, in order to get an effective preconditioner, it is crucial to preserve the structure of the coefficient matrix. On the other hand, thresholding iterative methods have been recently successfully applied to image deblurring problems, exploiting the sparsity of the image in a proper wavelet domain. Motivated by the results of recent papers, the main novelty of this work is combining nonstationary structure preserving preconditioners with general regularizing operators which hold in their kernel the key features of the true solution that we wish to preserve. Several numerical experiments shows the performances of our methods in terms of quality of the restorations.