Miracle-dz/UniFAR-FTUs huggingface dataset:
A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries

Introduction: Coronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue. Methods This study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error. Results Our results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability. Discussion The proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies.
Studies in Topics Going beyond the Standard Electroweak Model

Supersymmetric Standard Model has been built to solve the hierarchy problem which is encountered whenever Standard Model is incorporated into a theory with a larger mass scale. Experimentally, one of the first signals of physics beyond Standard Model is provided by the atmospheric neutrino experiments which indicate the existence of at least one non-zero neutrino mass. An interpretation of both solar and atmospheric neutrino anomalies in terms of neutrino oscillations would lead to specific patterns for neutrino masses and mixing. In this thesis, we studied the possibilities of generating the required neutrino mass patterns in the context of supersymmetric standard models with lepton number (L) violation. After an introduction to supersymmetric standard models in Chapter 1, we give an overview of neutrino phenomenology in chapter 2. Chapter 3 discusses analytical and semi-analytical solutions to Renormalisation Group Equations (RGE) with lepton number violating couplings in the superpotential. Neutrino masses with bilinear L violation in the context of gauge mediated supersymmetric models (GMSB) are discussed in Chapter 4, where 2-loop RGE for the parameter $\Delta B_i$ are presented. Chapter 5 discusses neutrino mass structure when L violation is through trilinear lepton number violating terms. Chapter 6 discusses RG enhancement of neutrino masses and neutrino mass bounds on trilinear lepton number violating couplings in the context of mSUGRA. Chapter 7 summarises general features of neutrino masses within these models and contains overall conclusions based on the work presented in the thesis.
Graph Theory for the Secondary School Classroom.

Graph Theory for the Secondary School Classroom by Dayna Brown Smithers After recognizing the beauty and the utility of Graph Theory in solving a variety of problems, the author concluded that it would be a good idea to make the subject available for students earlier in their educational experience. In this thesis, the author developed four units in Graph Theory, namely Vertex Coloring, Minimum Spanning Tree, Domination, and Hamiltonian Paths and Cycles, which are appropriate for high school level.
Principles and Practices of Unbiased Stereology: An Introduction for Bioscientists

Contents: Preface Introduction 1 The History of Stereology 2 Bias 3 Sampling 4 Geometric Probes 5 Bias in Estimating Number 6 The Disector Principle 7 Volume 8 Length and Surface Area 9 Nonstereological Bias 10 All Variation Considered 11 Typical Stereology Designs 12 Frequent Questions about Stereology Appendix. Conceptual Framework of Modern Stereology Glossary Bibliography Index
Contributions of Spherical Harmonics to Magnetic and Gravitational Fields

Gravitational forces are of cardinal importance in the dynamics of spacecraft; magnetic attractions sometime play a significant role also, as was the case with the Long Duration Exposure Facility, and as is now true for the first segment of Space Station Freedom. Both satellites depend on gravitational moment and a device known as a magnetic damper to stabilize their orientation. Magnetic fields are mathematically similar to gravitational fields in one important respect: each can be regarded as a gradient of a potential function that, in turn, can be described as an infinite series of spherical harmonics. Consequently, the two fields can be computed, in part, with quantities that need only be evaluated once, resulting in a savings of time when both fields are needed. The objective of this material is to present magnetic field and gravitational force expressions, and point out the terms that belong to both this is accomplished in Section 1 and 2. Section 3 contains the deductive reasoning with which one obtains the expressions of interest. Finally, examples in Section 4 show these equations can be used to reproduce others that arise in connection with special cases such as the magnetic field produced by a tilted dipole, and gravitational force exerted by an oblate spheroid. The mathematics are discussed in the context of terrestrial fields; however, by substituting appropriate constants, the results can be made applicable to fields belonging to other celestial bodies. The expressions presented here share the characteristics of algorithms set forth for computing gravitational force. In particular, computation is performed speedily by means of recursion formulae, and the expressions do not suffer from the shortcoming of a singularity when evaluated at points that lie on the polar axis.
A numerical investigation on active and passive scalars in isotropic compressible turbulence

In this paper, we investigated the statistical differences between active and passive scalars in isotropic compressible turbulence. In the inertial range, the kinetic energy and scalar have the Kolmogorov spectra, and the Kolmogorov and Obukhov-Corrsin constants are 2.06 and 0.87, respectively. The local scaling exponents computed from the second-order structure functions exist plateaus for velocity and active scalar, while that for passive scalar takes first a minimum of 0.61 then a maximum of 0.73. The mixed third-order structure function of velocity and passive scalar satisfies the 4/3-law. For the scaling exponent, the one of velocity and passive scalar mixing is between those of velocity and passive scalar, while the one of velocity and active scalar mixing is below those of velocity and active scalar. At large amplitudes, the p.d. f. of active scalar fluctuations is super-Gaussian, whereas that of passive scalar fluctuations is sub-Gaussian. Moreover, the p.d. f.s of the two scalar increments are concave and convex shapes, respectively, which exhibit strong intermittency at small scales, and approach Gaussian as scale increases. The active scalar has "ramp-cliff" structures, while the passive scalar seems to be dominated by rarefaction and compression. By employing a "coarse-graining" approach, we study scalar cascades. Unlike passive scalar, the cascade of active scalar is mainly determined by the viscous dissipation at small scales and the pressure-dilatation at large scales, where the latter is substantial in the vicinity of small-scale shocklets but is negligible after space averages, because of the cancelations between rarefaction and compression regions. Finally, in the inertial range, the p.d. f.s for the subgrid-scale fluxes of scalars can collapse to the same distribution, revealing the scale-invariant feature for the statistics of active and passive scalars.
Chimp: Efficient Lossless Floating Point Compression for Time Series Databases

Applications in diverse domains such as astronomy, economics and industrial monitoring, increasingly press the need for analyzing massive collections of time series data. The sheer size of the latter hinders our ability to efficiently store them and also yields significant storage costs. Applying general purpose compression algorithms would effectively reduce the size of the data, at the expense of introducing significant computational overhead. Time Series Management Systems that have emerged to address the challenge of handling this overwhelming amount of information, cannot suffer the ingestion rate restrictions that such compression algorithms would cause. Data points are usually encoded using faster, streaming compression approaches. However, the techniques that contemporary systems use do not fully utilize the compression potential of time series data, with implications in both storage requirements and access times. In this work, we propose a novel streaming compression algorithm, suitable for floating point time series data. We empirically establish properties exhibited by a diverse set of time series and harness these features in our proposed encodings. Our experimental evaluation demonstrates that our approach readily outperforms competing techniques, attaining compression ratios that are competitive with slower general purpose algorithms, and on average around 50% of the space required by state-of-the-art streaming approaches. Moreover, our algorithm outperforms all earlier techniques with regards to both compression and access time , offering a significantly improved trade-off between space and speed. The aforementioned benefits of our approach - in terms of all space requirements, compression time and read access - significantly improve the efficiency in which we can store and analyze time series data.
A method for computing two-dimensional turbulent flows

We present a new derivation of a closure to the mean equations describing two-dimensional turbulent flow. It is given to the equations for the mean and variance of the vorticity field. It is based on the use of a new general turbulent transport law which is derived for this purpose. The use of purely formal mathematical hypotheses is avoided. The validity of the closure may, in principle, be directly verified by experiment. The closed equations are applied to a study of the turbulent flow in a channel after special consideration is given to the formulation of proper boundary conditions. The variation of the mean flow field with Reynolds number is investigated. Numerical results show the presence of a bifurcation separating laminar and turbulent solutions and a drag crisis at low Reynolds number.
Introduction to Smooth Manifolds

Preface. - 1 Smooth Manifolds. - 2 Smooth Maps. - 3 Tangent Vectors. - 4 Submersions, Immersions, and Embeddings. - 5 Submanifolds. - 6 Sard's Theorem. - 7 Lie Groups. - 8 Vector Fields. - 9 Integral Curves and Flows. - 10 Vector Bundles. - 11 The Cotangent Bundle. - 12 Tensors. - 13 Riemannian Metrics. - 14 Differential Forms. - 15 Orientations. - 16 Integration on Manifolds. - 17 De Rham Cohomology. - 18 The de Rham Theorem. - 19 Distributions and Foliations. - 20 The Exponential Map. - 21 Quotient Manifolds. - 22 Symplectic Manifolds. - Appendix A: Review of Topology. - Appendix B: Review of Linear Algebra. - Appendix C: Review of Calculus. - Appendix D: Review of Differential Equations. - References. - Notation Index. - Subject Index
Higgs mass determined by cosmological parameters

Postulating that all massless elementary fields have conformal scaling symmetry removes a conflict between gravitational theory and the standard model of elementary quantum fields. If the scalar field essential to SU(2) symmetry breaking has conformal symmetry, it must depend explicitly on the Ricci curvature scalar of gravitational theory. This has profound consequences for both cosmology and elementary particle physics, since cosmological data determine scalar field parameters. A modified Friedmann equation is derived and solved numerically. The theory is consistent with all relevant data for supernovae redshifts below $z=1$. The implied value of the cosmological constant implies extremely small Higgs mass, far below current empirical lower bounds. Detection of a Higgs boson with large mass would falsify this argument.
The Sloan Digital Sky Survey Data Release 7 M Dwarf Spectroscopic Catalog

We present a spectroscopic catalog of 70,841 visually inspected M dwarfs from the seventh data release (DR7) of the Sloan Digital Sky Survey (SDSS). For each spectrum, we provide measurements of the spectral type, a number of molecular bandheads, and the H-alpha, H-beta, H-gamma, H-delta and Ca II K emission lines. In addition, we calculate the metallicity-sensitive parameter zeta and 3D space motions for most of the stars in the sample. Our catalog is cross-matched to Two Micron All Sky Survey (2MASS) infrared data, and contains photometric distances for each star. Future studies will use these data to thoroughly examine magnetic activity and kinematics in late-type M dwarfs and examine the chemical and dynamical history of the local Milky Way.
Role of phonons in Josephson oscillations of excitonic and polaritonic condensates

We analyze theoretically the role of the exciton-phonon interactions in phenomena related to the Josephson effect between two spatially separated exciton and exciton-polariton condensates. We consider the role of the dephasing introduced by phonons in such phenomena as Josephson tunneling, self-trapping and spontaneous polarization separation. In the regime of cw pumping we find a remarkable bistability effect arising from exciton-exciton interactions as well as regimes of self-sustained regular and chaotic oscillations.
Energy and exergy analysis of solar power tower plants Applied Thermal Engineering

Establishing the renewable electricity contribution from solar thermal power systems based on energy analysis alone cannot legitimately be complete unless the exergy concept becomes a part of that analysis. This paper presents a theoretical framework for the energy analysis and exergy analysis of the solar power tower system using molten salt as the heat transfer ﬂ uid. Both the energy losses and exergy losses in each component and in the overall system are evaluated to identify the causes and locations of the thermodynamic imperfection. Several design parameters including the direct normal irradiation (DNI), the concentration ratio, and the type of power cycle are also tested to evaluate their effects on the energy and exergy performance. The results show that the maximum exergy loss occurs in the receiver system, followed by the heliostat ﬁ eld system, although main energy loss occurs in the power cycle system. The energy and exergy ef ﬁ ciencies of the receiver and the overall system can be increased by increasing the DNI and the concentration ratio, but that increment in the ef ﬁ ciencies varies with the values of DNI and the concentration ratio. It is also found that the overall energy and exergy ef ﬁ ciencies of the solar tower system can be increased to some extent by integrating advanced power cycles including reheat Rankine cycles and supercritical Rankine cycles.
Partition Algorithm for System Identification

We review the fundamentals of filtering theory and of the Lainiotis Partition Algorithm for system identification. Original reults are presented on the question of convergence of the Lainiotis algorithm; namely convergence is proved (within the algorithm’s resolution) as long as certain exponential expectation conditions are satisfied. The algorithm is then applied to the identification of siumlated systems as well the identification of a real DC motor.
An ELECTRA Model for Latin Token Tagging Tasks

This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Brepols’ ReFlex software. The results are decent, but suffer from inconsistencies between Brepols’ and EvaLatin’s definitions of a lemma. For POS-tagging, the results come up just short from the first place in this competition, mainly struggling with proper nouns. For morphological tagging, there is much more room for improvement. Here, the constraints added to our Multiclass Multilabel model were often not tight enough, causing missing morphological features. We will further investigate why the combination of the different morphological features, which perform fine on their own, leads to issues.
Monocular Facial Performance Capture Via Deep Expression Matching

Facial performance capture is the process of automatically animating a digital face according to a captured performance of an actor. Recent developments in this area have focused on high‐quality results using expensive head‐scanning equipment and camera rigs. These methods produce impressive animations that accurately capture subtle details in an actor's performance. However, these methods are accessible only to content creators with relatively large budgets. Current methods using inexpensive recording equipment generally produce lower quality output that is unsuitable for many applications. In this paper, we present a facial performance capture method that does not require facial scans and instead animates an artist‐created model using standard blendshapes. Furthermore, our method gives artists high‐level control over animations through a workflow similar to existing commercial solutions. Given a recording, our approach matches keyframes of the video with corresponding expressions from an animated library of poses. A Gaussian process model then computes the full animation by interpolating from the set of matched keyframes. Our expression‐matching method computes a low‐dimensional latent code from an image that represents a facial expression while factoring out the facial identity. Images depicting similar facial expressions are identified by their proximity in the latent space. In our results, we demonstrate the fidelity of our expression‐matching method. We also compare animations generated with our approach to animations generated with commercially available software.
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022

We participate in the LT4HALA2022 shared task EvaHan. This task has two subtasks. Subtask 1 is word segmentation, and subtask 2 is part-of-speech tagging. Each subtask consists of two tracks, a close track that can only use the data and models provided by the organizer, and an open track without restrictions. We employ three pre-trained models, two of which are open-source pre-trained models for ancient Chinese (Siku-Roberta and roberta-classical-chinese), and one is our pre-trained GlyphBERT combined with glyph features. Our methods include data augmentation, data pre-processing, model pretraining, downstream fine-tuning, k-fold cross validation and model ensemble. We achieve competitive P, R, and F1 scores on both our own validation set and the final public test set.
A Parallel Convolutions and Decision Fusion Based Flower Classification Method

: Flower classification is of great significance to the fields of plants, food, and medicine. However, due to the inherent inter-class similarity and intra-class differences of flowers, it is a difficult task to accurately classify them. To this end, this paper proposes a novel flower classification method which combines enhanced VGG16 (E-VGG16) with decision fusion. Firstly, facing the shortcomings of the VGG16, an enhanced E-VGG16 is proposed. E-VGG16 introduces a parallel convolution blocks designed in this paper on VGG16, combined with several other optimizations to improve the quality of extracted features. Secondly, considering the limited decision-making ability of a single E-VGG16 variant, parallel convolutional blocks are embedded in different positions of E-VGG16 to obtain multiple E-VGG16 variants. By introducing information entropy to fuse multiple E-VGG16 variants for decision-making, the classification accuracy is further improved. The experimental results on the Oxford Flower102 and Oxford Flower17 public datasets show that the classification accuracy of our method reaches 97.69% and 98.38%, respectively, which significantly outperforms the state-of-the-art methods.
New HPLC Method for Determination of Cefquinome in Cattle Plasma and its Application for Pharmacokinetic Study

The cefquinome samples from plasma were extracted and separated by HPLC on a reversed phase C-18 column with mobile phase of acetonitrile and formic acid (0.1%) solution- (90:10, v/v). The calibration curves were linear ranged from 0.01-5µg/mL. The limit of quantification (LOQ) and limit of detection (LOD) were 0.04 and 0.01 µg/mL. The recovery of cefquinome from plasma was 73.4%, 78.33%, and 77% for low, medium and high concentration samples. The intraday and inter day relative standard deviation were less than 15%. The Cefquinome were found to be stable for more than 3 month at -70 o C (degree symbol) and more than 24 h at 4 o C. The present method has been applied successfully for the pharmacokinetics study in cattle. After intramuscular administration, the mean peak serum Concentrations (C max ) was found 2. 95μg/mL and achieved after (T max ) 1.50h. The elimination half-life (t l/2(el) ) was 2.03±0.06h. The Mean Residence Time (MRT) was 2.58±0.05h and the area under curve from zero time to infinity (AUC 0-∞ ) were 9.67±0. 72μg/mL/h. The newly proposed method was shown to be simple, rapid, economical and sensitive compared to the previously reported method, and thereafter applicable for the pharmacokinetics study in animals.
Co-Designing AI-Based Orchestration Tools to Support Dynamic Transitions: Design Narratives Through Conjecture Mapping

: Dynamically transitioning between individual and collaborative learning has been hypothesized to have positive effects, such as providing the optimal learning mode based on students’ needs. There are, however, challenges in orchestrating these transitions in real-time while managing a classroom of students. AI-based orchestration tools have the potential to alleviate some of the orchestration load for teachers. In this study, we describe a sequence of three design sessions with teachers where we refine prototypes of an orchestration tool to support dynamic transitions. We leverage design narratives and conjecture mapping for the design of our novel orchestration tool. Our contributions include the orchestration tool itself; a description of how novel tool features were revised throughout the sessions with teachers, including shared control between teachers, students, and AI and the use of AI to support dynamic transitions , and a reflection of the changes to our design and theoretical conjectures. support dynamic transitions
Stata Tip 63: Modeling Proportions

You may often want to model a response variable that appears as a proportion or fraction: the share of consumers’ spending on food, the fraction of the vote for a candidate, or the fraction of days when air pollution is above acceptable levels in a city. To handle these data properly, you must take account of the bounded nature of the response. Just as a linear probability model on unit record data can generate predictions outside the unit interval, using a proportion in a linear regression model will generally yield nonsensical predictions for extreme values of the regressors. One way to handle this for response variables’ values strictly within the unit interval is the logit transformation y = 1