Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Jets and X-Ray Plateaus in Gamma-Ray Burst Phenomena

The first multi-messenger detection of a binary neutron star merger, GW170817, brought to the forefront the structured jet model as a way to explain multiwavelength observations taken more than a year after the event. Here, we show that the high-latitude emission from a structured jet can naturally produce an X-ray plateau in gamma-ray burst (GRB) light curves, independent of the radiation from an external shock. We calculate the radiation from a switched-off shell featuring an angular structure in both its relativistic bulk motion and intrinsic brightness. Our model is able to explain the shallow decay phase (plateau) often observed in GRB X-ray light curves. We discuss the possible contribution of the structured jet high-latitude emission to other distinctive features of GRB X-ray light curves, and its capability to explain the chromatic optical/X-ray light-curve properties.
Revitalizing the Roles of EFL Teachers Forum for Developing Teachers Professionalism

: EFL Teachers Forum becomes the most famous teacher forum to support teacher professional development in Indonesia. Since, it is held on the whole areas geographically from urban to the rural area. It is expected to be the best way to develop teacher’s competence and skill in learning and teaching process. This study analyzes the role and evaluates EFL teacher forum that need to be revitalized for developing teacher professionalism. Using case study methods, the findings indicate that EFL teacher forum still has not covered the innovative learning and teaching strategy. This forum is very focused in the socialization of the new curriculum called K-2013 and how it is implemented in the learning equipment such as lesson plan, syllabus and assessment aspect. As suggestion of this research, EFL Teachers Forum has to be concerned in innovative learning and teaching strategies based on technology.
Two is Better Than One: The Case for 2-Tree for Skewed Data Sets

Real-world data sets almost always exhibit skew, i.e., a majority of the accesses go to a minority of the records. Obviously, Smith and Brown are more popular names than Stonebraker and Graefe. Traditional block-oriented index structures such as B-trees are sub-optimal for skewed data because an index block often has a small number of hot records and a larger number of cold ones. This results in poor main memory utilization and increased cost. To alleviate this problem, we propose a 2-Tree architecture, where hot index records are in one tree and cold ones are in a second tree. Hot tree blocks are frequently accessed and likely to remain in main memory, resulting in improved main memory utilization. Our core idea is to employ a lightweight general migration protocol to move records between trees in both directions when appropriate and to maintain access statistics at low cost. In addition, the two trees can be configured separately for hardware differences. One tree can be optimized for main memory while the second exploits secondary storage. Obviously, the 2-Tree idea can also be generalized to multiple storage levels and/or devices. We show how the 2-Tree idea and record migration can be applied to both B+trees and LSM-trees to improve their memory utilization significantly (by 15 × and 20 × respectively) on a highly skewed workload. We also observed up to 1.7 × throughput improvement on a Zipfian-skewed IO-bound workload compared to traditional single B+tree or LSM-tree using the same amount of main memory. Unlike existing solutions for improving memory utilization at the cost of inferior range scan performance, 2-Tree refuses to make such a compromise.
Package ‘mHMMbayes’

October 13, 2022 Type Package Title Multilevel Hidden Markov Models Using Bayesian Estimation Version 0.2. 0 Depends R (>= 3.5.0) Imports MCMCpack, mvtnorm, stats, Rdpack, Rcpp Maintainer Emmeke Aarts <e.aarts@uu.nl> Description An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017. 1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with a categorical distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.
A Meta-Analytic Estimation of Its Antecedents and Consequences

: 0 throughout the past four decades, researchers have examined several antecedents and consequences of international marketing program standardization. However, the findings reported in the literature are too fragmented to yield clear insights. to address this issue, the authors conduct a meta-analysis to quantitatively synthesize and analyze the empirical findings on antecedents and consequences of international marketing program standardization. 0 the empirical results reported in this study have been integrated from 110 independent samples published in 108 articles. Multivariate analysis is used to examine the antecedents of international marketing program standardization strategy as well as the interdependence between the four elements of the marketing-mix. in addition, meta-regression analyses and subgroup analyses are performed to test potential moderating effects of performance measurement characteristics on marketing standardization-performance relationship. 0 On the basis of the results, the authors discuss the implications of their findings and provide directions for further research.
NONLINEAR DYNAMICS OF CELLULAR FLAMES

The problem of a flame stabilized by a line source of fuel in the cellular regime, i.e., $L < 1$,where L is the Lewis number, is solved numerically. It is found that as L is decreased, transitions from stationary axisymmetric to stationary cellular to nonstationary cellular flames occur. The nonstationary cellular flames can exhibit both periodic and quasi-periodic dynamics. In particular, as L is decreased successive transitions from stationary axisymmetric solutions, to stationary four-cell solutions, to spinning four-cell solutions are computed. The spinning four-cell solutions are very slowly traveling waves that arise due to an infinite period, symmetry breaking bifurcation, in which the reflection symmetry of the stationary four-cell solution is broken. Near the transition point, the traveling wave solution branch is unstable and perturbations evolve to either a stationary five-cell or a nonstationary mixed-mode solution exhibiting apparently quasi-periodic dynamics. If L is further decreased beyond.
Fractal-based stochastic inversion of poststack seismic data using very fast simulated annealing

Seismic data do not contain some low- and high-frequency information because of the band-limited nature of the source wavelet. A deterministic inversion of such band-limited seismic data produces smooth models which are devoid of high-frequency variations observed in well logs. Stochastic inversion methods often based on random Gaussian priors can have a limitation of producing high frequencies in the desired model particularly the frequency band not constrained by the input seismic data. In this paper, we propose a new stochastic poststack inversion algorithm where fractal models constructed from statistical properties of well logs are used to generate a priori models. This provides a high-resolution model without injecting spurious high-frequency estimates in model space. Stacked seismic data are used in the inversion in which a suitable objective function is minimized using a nonlinear optimization method called 'very fast simulated annealing'. We demonstrate the effectiveness of our method for the estimation of acoustic impedance with the application to a field dataset.
Hypersurfaces in space forms satisfying the condition Δ

In this work we study and classify pseudo-Riemannian hypersur-faces in pseudo-Riemannian space forms which satisfy the condition Ax = Ax + B , where A is an endomorphism, B is a constant vector, and x stands for the isometric immersion. We prove that the family of such hypersurfaces consists of open pieces of minimal hypersurfaces, totally umbilical hypersur-faces, products of two nonflat totally umbilical submanifolds, and a special class of quadratic hypersurfaces.
A Solution to the Contact of Two Rough Spherical Surfaces

The influence of roughness on the interfacial pressure distribution in the contact of two spherical surfaces is considered. An approximate general solution is presented and it is shown that the pressure for all radii can be determined solely from a reference pressure which is, in turn, a function of two parameters - a dimensionless roughness and a dimensionless hardness. Values for the radius of contact are also presented.
Comparative study of hole transport in poly(p-phenylene vinylene) derivatives

TNO Institute of Industrial Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands~Received 17 August 1999! The dc mobility of holes in four poly(p-phenylene vinylene! ~PPV! derivatives ~three fully conjugatedpolymers with different side chains and one partially conjugated PPV! is examined as a function of temperatureT and applied electrical ﬁeldE. In all cases the mobility mfollows the empirical m}exp g
Simulation-Extrapolation: The Measurement Error Jackknife

Abstract This article provides theoretical support for our simulation-based estimation procedure, SIMEX, for measurement error models. We do so by establishing a strong relationship between SIMEX estimation and jackknife estimation. A result of our investigation is the identification of a variance estimation method for SIMEX that parallels jackknife variance estimation. Data from the Framingham Heart Study are used to illustrate the variance estimation procedure in logistic regression measurement error models.
Holographic and weak-phase projection system for 3D shape reconstruction using temporal phase unwrapping

Two projection systems that use an LCoS phase modulator are proposed for 3D shape reconstruction. The LCoS is used as an holographic system or as a weak phase projector, both configurations project a set of fringe patterns that are processed by the technique known as temporal phase unwrapping. To minimize the influence of camera sampling, and the speckle noise in the projected fringes, an speckle noise reduction technique is applied to the speckle patterns generated by the holographic optical system. Experiments with 3D shape reconstruction of ophthalmic mold and other testing specimens show the viability of the proposed techniques.
Hemispherical Analyser with 2-D PSD for Zero-degree Auger Projectile Spectroscopy

Details of a new high gain zero-degree Auger projectile electron spectrograph using a hemispherical analyser and a 2-dimensional position sensitive detector (PSD) with multichannel plates and a resistive anode encoder are presented. A four-element lens mounted at the entrance of the analyser, provides a virtual slit for the incoming electrons by focusing them while at the same time decelerating them to improve their energy resolution. Electrons enter through an aperture at a position R0 which is displaced (along the energy dispersion axis) with respect to the commonly used central entrance position at ½(R1 + R2). The analyser has an acceptance energy range of 20% and an energy resolution of 0.9%. An ion-optics trajectory simulation indicates improved focusing properties for this off-center position thus avoiding the need for cumbersome fringing field correction schemes. Test measurements of high resolution projectile Auger spectra produced in 21.7 MeV collisions of F8+ and F7+ projectiles with H2 and He are presented.
Fundamental matrix and planar homographies in stereo vision

We describe the epipolar constraint that specifies the geometry of stereo vision. We consider the 3D structure reconstruction from multiple views through the new perspective of basing the reconstruction from directly estimated planar homographies instead of using techniques that are based on matched point pairs. Planar homography parameters can more accurately extract scene planar surfaces and directly solve for the 3D structure and camera motion parameters. The new method has the advantage that it integrates larger amount of information because the homography parameters are estimated directly from the intensities and not from an abstracted descriptor of the neighborhood. Because it does not rely on a transformation of an entire image region, the method is efficient.
Ameliorative effects of Portulaca oleracea L. (purslane) and its active constituents on nervous system disorders: A review

Nowadays, the global interest in the use of herbal medicines and their main components in developing novel effective medications with fewer adverse effects is rising. Precise medicinal plants have potential advantageous applications for several neurodegenerative disorders. Portulaca oleracea L. (purslane) belongs to the Portulacaceae Juss family. In folk medicine, it has been used as a febrifuge, antiseptic, vermifuge, and in treating arthritis, burns, cough, headache, intestine, stomach, liver disorders, as well as shortness of breath. Pharmacological investigations have also disclosed its antioxidant, anti-inflammatory, anti-apoptotic, immunomodulatory, antidepressant, anxiolytic, and neuroprotective properties. The current work prepared an updated and broad literature review on purslane highlighting its therapeutic effects on some nervous system disorders. It has been shown that P. oleracea and its active constituents have considerable neuroregenerative, neuroprotective, and antinociceptive properties. Accordingly, our team classified and discussed the outcomes of some nervous system disorders comprising Alzheimer’s disease, Parkinson’s disease, depression, epilepsy, anxiety, psychosis, drug dependence, hypoxia, and pain; although, additional preclinical and clinical assessments are necessary to reinforce the beneficial effects of purslane on nervous system disorders.
BAND-EDGE PHOTOREFRACTIVITY IN SEMICONDUCTORS : THEORY AND EXPERIMENT

At wavelengths close to the band edge, strong photorefractive gratings using the Franz–Keldysh electrorefractive effect can be written in semiconductors. Two‐beam‐coupling exponential gain coefficients as high as Γ=16.3 cm−1 have been obtained in GaAs by combining the electrorefractive photorefractive grating with the conventional electro‐optic photorefractive grating and using the moving grating technique to enhance the photorefractive space‐charge field. A method for calculation of the gain coefficient near the band edge of materials is presented. The method is applied to GaAs and the results are compared to the experimental data. Reasonable agreement with experiment has been achieved. An optimal spectral range (910 nm<λ<930 nm) for near‐band‐edge photorefractivity in GaAs has been found. Conventional theories of photorefractivity based on Kukhtarev’s equations are found to be sufficient for calculation of the photorefractive space‐charge field near the band edge. Predictions of the gain coefficient nea.
Motion of a superfluid vortex according to holographic quantum dissipation

Vortices are topological defects associated with superfluids and superconductors, which, when mobile, dissipate energy destroying the dissipation-less nature of the superfluid. The nature of this"quantum dissipation"is rooted in the quantum physical nature of the problem, which has been subject of an extensive literature. However, this has mostly be focused on the measures applicable in weakly interacting systems wherein they are tractable via conventional methods. Recently it became possible to address such dynamical quantum thermalization problems in very strongly interacting systems using the holographic duality discovered in string theory, mapping the quantum problem on a gravitational problem in one higher dimension, having as benefit offering a more general view on how dissipation emerges from such intricate quantum physical circumstances. We study here the elementary problem of a single vortex in two space dimensions, set in motion by a sudden quench in the background superflow formed in a finite density"Reissner-Nordstrom"holographic superconductor. This reveals a number of surprising outcomes addressing questions of principle. By fitting the trajectories unambiguously to the Hall-Vinen-Iordanskii phenomenological equation of motion we find that these are characterized by a large inertial mass at low temperature that however diminishes upon raising temperature. For a weak drive the drag is found to increase when temperature is lowered which reveals a simple shear drag associated with the viscous metallic vortex cores, supplemented by a conventional normal fluid component at higher temperatures. For a strong drive we discover a novel dynamical phenomenon: the core of the vortex deforms accompanied by a large increase of the drag force.
Asymptotic Analysis of Harmonic Maps With Prescribed Singularities

Motivated by stationary vacuum solutions of the Einstein ﬁeld equations, we study singular harmonic maps from domains of 3-dimensional Euclidean space to the hyperbolic plane having bounded hyperbolic distance to Kerr harmonic maps. In the degenerate case, we prove that every such harmonic map admits a unique tangent harmonic map at the extreme black hole horizon. The possible tangent maps are classiﬁed and shown to be shifted ‘extreme Kerr’ geodesics in the hyperbolic plane that depend on two parameters, one determined by angular momentum and another by conical singularities. In addition, rates of convergence to the tangent map are established. Similarly, expansions in the asymptotically ﬂat end are presented. These results, together with those of Li-Tian [27, 28] and Weinstein [41, 42], provide a complete regularity theory for harmonic maps from R 3 \ z -axis to H 2 with prescribed singularities. Lastly, the analysis is utilized to prove existence of the so called near horizon limit, and to compute the associated near horizon geometries of extreme black holes.
Topical Hidden Genome: Discovering Latent Cancer Mutational Topics using a Bayesian Multilevel Context-learning Approach

Statistical inference on the cancer-site speciﬁcities of collective ultra-rare whole genome somatic mutations is an open problem. Traditional statistical methods cannot handle whole-genome mutation data due to their ultra-high-dimensionality and extreme data sparsity – e.g., >30 million unique variants are observed in the ~1700 whole-genome tumor dataset considered herein, of which >99% variants are encountered only once. To harness information in these rare variants we have recently proposed the “hidden genome model”, a formal multilevel multi-logistic model that mines information in ultra-rare somatic variants to characterize tumor types. The model condenses signals in rare variants through a hierarchical layer leveraging contexts of individual mutations. The model is currently implemented using consistent, scalable point estimation techniques that can handle 10s of millions of variants detected across thousands of tumors. Our recent publications have evidenced its impressive accuracy and attributability at scale. However, principled statistical inference from the model is infeasible due to the volume, correlation, and non-interpretability of the mutation contexts. In this paper we propose a novel framework that leverages topic models from the ﬁeld of computational linguistics to induce an interpretable dimension reduction of the mutation contexts used in the model. The proposed model is implemented using an eﬃcient MCMC algorithm that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of out-of-the-box high-dimensional multi-class regression methods and software. We employ our model on the Pan Cancer Analysis of Whole Genomes (PCAWG) dataset, and our results reveal interesting novel insights.
Proof of Swarm Based Ensemble Learning for Federated Learning Applications

Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combine results of local models to produce a global model. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. This is because, in such methods predictions of some of the peers are disregarded, so a majority of peers can win without even considering other peers' decisions. Additionally, the confidence score of the result of each peer is not normally taken into account, although it is an important feature to consider for ensemble learning. Moreover, the problem of a tie event is often left un-addressed by methods such as BFT. To fill these research gaps, we propose PoSw (Proof of Swarm), a novel distributed consensus algorithm for ensemble learning in a federated setting, which was inspired by particle swarm based algorithms for solving optimisation problems. The proposed algorithm is theoretically proved to always converge in a relatively small number of steps and has mechanisms to resolve tie events while trying to achieve sub-optimum solutions. We experimentally validated the performance of the proposed algorithm using ECG classification as an example application in healthcare, showing that the ensemble learning model outperformed all local models and even the FL-based global model.