Miracle-dz/UniFAR-FTUs huggingface dataset:
Routine clinical heart examinations using SQUID magnetocardiography at University of Tsukuba Hospital

A 64-channel Nb-based DC-SQUID magnetocardiography (MCG) system was installed at the University of Tsukuba Hospital (UTH) in March 2007 after obtaining Japanese pharmaceutical approval and insurance reimbursement approval. In the period between 2008 and 2016, the total number of patients was 10 085. The heart diseases diagnosed in fetuses as well as adults are mainly atrial arrhythmia, abnormal repolarization, ventricular arrhythmia, and fetal arrhythmia. In most cases of insufficient diagnostic accuracy with electrocardiography, SQUID MCG precisely revealed these heart diseases as an abnormal electrical current distribution. Based on success in routine examinations, SQUID MCG is now an indispensable clinical instrument with diagnostic software tuned up during routine use at UTH.
Guaranteed systematic simulation of discrete‐time systems defined by polynomial expressions via convex relaxations

This paper concerns the simulation of a class of nonlinear discrete‐time systems under a set of initial conditions described by a bounding ellipsoid. We derive a procedure allowing the propagation of such ellipsoids through time, which makes it possible to set a guaranteed hard bound on the evolution of the state of the system for all the possible initial conditions. Two versions of this procedure are given, the second of which is slightly less general but less computationally demanding. At the end of this paper, we first show an application of the method in the domain of aerospace engineering. Subsequently, three academic examples of applications are presented, two of which come from the theory of fractals.
Religious Decline as a Population Dynamic: Generational Replacement and Religious Attendance in Europe

Abstract:Nothing puzzles sociologists of religion more than the reasons and the patterns behind religious change. In the literature on secularization processes, it is broadly accepted that ongoing modernization has undermined many of the pillars of institutional religions, leading to widespread religious decline. While a considerable body of research investigates the "why(s)" of this decline, the main focus of this paper is on the "how. " Drawing on CARPE, a harmonized dataset concerning church attendance with almost 2 million observations spanning over 40 years in 39 European countries, this article disentangles the contributions of period change and cohort replacement to the general religious decline. It shows that this decline reflects a real population dynamic based on generational replacement. It is not historical events or developments affecting everyone that undermine religion: new and less religious cohorts are replacing old and more religious ones. Like many other population dynamics, this process is described by an S-shaped curve and applies—with different speeds and levels—to almost all the European countries considered herein. These findings suggest that the effects of the processes of modernization on the mechanisms of religious socialization should be scrutinized more carefully when studying the reasons behind religious decline.
Bifurcation Analysis for Metapopulation Models

Two models with four components of chain populations are considered. In the model, a prey population X is predated by individuals of a specialist predator population Y, and another prey population Z is predated by individuals of a generalist predator population U. This model is governed by a system of four nonlinear first order ordinary differential equations. To study the dynamics of the food chain model, the mentioned system of ordinary differential equations solved numerically. One of the biological parameters varied in a sufficiently large range and its effects on the dynamics of the system are observed. Along the  w axis of the predating rate of the specialist predatorY , around four points , 088 . 0 , 045 . 0 2 1   w w 920 . 0 , 3000 . 0 4 3   w and w we meet chaos. At each
SIW‐based cavity‐backed self‐diplexing antenna with plus‐shaped slot

A substrate integrated waveguide (SIW)‐based planar cavity‐backed self‐diplexing antenna, designed for X‐band applications, is presented in this article. The proposed antenna is fed by two orthogonal 50 Ω microstrip lines. Two orthogonal rectangular slots, combination of which makes a plus‐shaped slot, of different lengths are etched on the top of the SIW cavity. Due to the unequal arm lengths of the plus‐shaped slot, the antenna radiates at two distinct frequencies. An isolation of better than 20 dB is achieved between the two ports and hence, the proposed antenna exhibits self‐diplexing characteristics. The antenna offers a gain of 5.7 dBi and 5.94 dBi at 8.55 GHz and 9.77 GHz, respectively.
An Improved K-means Clustering Algorithm For Data Mining

Data clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. K-means is an iterative algorithm in which the number of clusters must be determined before the execution.In this book an efficient k-means algorithm is proposed. Since, in each iteration, the k-means algorithm computes the distances between data point and all centers, this is computationally very expensive especially for huge data sets. For each data point, we can keep the distance to the nearest cluster. At the next iteration, we compute the distance to the previous nearest cluster. If the new distance is less than or equal to the previous distance, the point stays in its cluster, and there is no need to compute its distances to the other cluster centers. This saves the time required to compute distances to k−1 clusters. Experimental results show the accuracy and effectiveness of the proposed method.
QCD Equations of State in Hadron–Quark Continuity

The properties of dense matter in quantum chromodynamics (QCD) are delineated through equations of state constrained by the neutron star observations. The two solar mass constraint, the radius constraint of ≃11–13 km, and the causality constraint on the speed of sound, are used to develop the picture of hadron–quark continuity in which hadronic matter continuously transforms into quark matter. A unified equation of state at zero temperature and β-equilibrium is constructed by a phenomenological interpolation between nuclear and quark matter equations of state.
Quantum state transfer via a hybrid solid–optomechanical interface*

We propose a scheme to implement quantum state transfer between two distant quantum nodes via a hybrid solid–optomechanical interface. The quantum state is encoded on the native superconducting qubit, and transferred to the microwave photon, then the optical photon successively, which afterwards is transmitted to the remote node by cavity leaking, and finally the quantum state is transferred to the remote superconducting qubit. The high efficiency of the state transfer is achieved by controllable Gaussian pulses sequence and numerically demonstrated with theoretically feasible parameters. Our scheme has the potential to implement unified quantum computing–communication–computing, and high fidelity of the microwave–optics–microwave transfer process of the quantum state.
"I wouldn't say that I'm overly campy": The socio-cultural subjugation of femininity within gay socio-sexual applications

Abstract:The prevalence of femmephobic attitudes amongst gay, bisexual, and queer (GBQ) men is well-reported, yet under-discussed employing femme theory. Femmephobia, or the socio-cultural regulation of femininity, is common within GBQ online socio-sexual applications, yet more research is needed to analyze the different ways femmephobia takes place within GBQ socio-sexual applications predominately used by GBQ men. This study analyzes qualitative data from semi-structured interviews with 79 participants who described their experiences using socio-sexual applications and interacting with online outreach and health information workers. Participants described the different ways femmephobia and the regulation of femininity manifests within GBQ socio-sexual applications and how such forms of regulation relate to self-presentation and the privileging of masculinity within GBQ socio-sexual applications. Participants further described how femmephobia and the denigration of femininity intersected with anti-Asian racism online.
Jet production measurements at the ATLAS experiment

Jet production is the dominant process at high transverse momentum in proton-proton collisions at the Large Hadron Collider and allows for a precise test of the strong interaction and the structure of the proton. The inclusive jet cross-section has been measured with the ATLAS detector at two centre-ofmass energies of sqrt(s) = 2.76 TeV and sqrt(s) = 7 TeV. The datasets correspond to an integrated luminosity of 0.20 pb-1 and 37 pb-1, respectively. Based on jets that are identified using the anti-kt algorithm with two radius parameters of 0.4 and 0.6, the double-differential inclusive jet cross-section is presented as a function of the jet transverse momentum p_T and seven ranges of the jet rapidity y. The measurement comprised the calibration of the luminosity detectors for sqrt(s) = 2.76 TeV, where an uncertainty of 2.7% has been achieved. The ratio of the cross-section measurements is determined as a function of transverse momentum as well as the dimensionless quantity x_T = 2p_T / sqrt(s). The systematic uncertainties on the ratio is significantly reduced due to the cancellation of correlated uncertainties in the two measurements. Results are compared to predictions from perturbative QCD calculations at next-to-leading order precision corrected for non-perturbative e�ffects, and predictions from parton shower Monte Carlo simulations with next-to-leading order matrix elements. Several PDF sets, parton shower tunes and Monte Carlo simulations are compared. In addition, the jet cross-section data are analysed within a framework of next-to-leading order perturbative QCD calculations to determine the parton distribution functions of the proton, together with data from HERA.
Instantaneous Flow Reconstruction from Particle Trajectories with Vortex-in-Cell

The manuscripts presents the working principle of a novel technique to interpolate sparse and scattered particle tracking velocimetry (PTV) measurements onto a dense grid, by using the velocity measurements along a full particle trajectory. The method performs iteratively a vortex-in-cell simulation (Schneiders et al. 2014 in Exp Fluids 55:1692) to minimize the difference between the simulation and the scattered velocity measurements. The optimization is solved using the L-BFGS algorithm where gradients are evaluated efficiently using the adjoint of the vortex-in-cell code. In the numerical assessment, the case of a vortex is considered. At a given seeding concentration, the reconstruction quality of velocity was shown to improve by 50% when a full track is used in comparison to a divergence free reconstruction using only instantaneous velocity. The numerical assessment demonstrates the potential of the method to increase measurement quality and spatial resolution, in comparison to techniques that interpolate velocity using only instantaneous flow information and for example the divergence free constraint.
Reiinakano/Scikit-Plot: V0.2.8

Features New option hide_zeros for plot_confusion_matrix by @ExcaliburZero. #39 New option to plot only certain labels in plot_confusion_matrix by @ExcaliburZero. #41 New options to set colormaps for plot_pca_2d_projection , plot_silhouette , plot_precision_recall_curve , plot_roc_curve , and plot_confusion_matrix . #50 Bugfix: Fixed bug with nan values in confusion matrices by @ExcaliburZero (#42)
Subgroup Identification in Clinical Trials by Stochastic SIDEScreen Methods

ABSTRACT Subgroup identification for personalized medicine has become very popular in the last decade. Efficient recursive partitioning procedures adapted from machine learning are natural approaches for performing subgroup identification based on pre-defined biomarkers since they provide subgroups as terminal nodes in the decision tree. However, recursive partitioning is also known as a potentially unstable procedure with results being quite sensitive to normal sampling variability in the data. One common approach, borrowed from ensemble learning, to overcome such instability is application of recursive partitioning to multiple data sets sampled from the observed data followed by averaging the results over the collection of subgroups. This article proposes an alternative approach to subgroup identification in clinical trials that first evaluates the predictive strength of biomarkers based on variable importance and then applies recursive partitioning to the biomarkers with the highest variable importance scores. A deterministic version of this idea was implemented in the Adaptive SIDEScreen method that generates a collection of patient subgroups by retaining multiple candidate splits of each parent group by different biomarkers (Lipkovich and Dmitrienko 2014a, 2014b). Now, we extend the Adaptive SIDEScreen and introduce the Stochastic SIDEScreen method. The key idea is to introduce randomness in the subgroup generation process, borrowing from bagging methods, to produce a broader collection of subgroups. Specifically, the SIDES method, where the most promising biomarkers are selected for each parent group from a set of candidate biomarkers, is applied to multiple bootstrap samples of the data. This new approach leads to a more reliable biomarker selection process, which is especially important for smaller, early phase studies when biomarker selection is typically carried out. The method is illustrated using clinical trial examples.
Coherent Microwave Scattering Model of Marsh Grass

In this work, we developed an electromagnetic scattering model to analyze radar scattering from tall‐grass‐covered lands such as wetlands and marshes. The model adopts the generalized iterative extended boundary condition method (GIEBCM) algorithm, previously developed for buried cylindrical media such as vegetation roots, to simulate the scattering from the grass layer. The major challenge of applying GIEBCM to tall grass is the extremely time‐consuming iteration among the large number of short subcylinders building up the grass. To overcome this issue, we extended the GIEBCM to multilevel GIEBCM, or M‐GIEBCM, in which we first use GIEBCM to calculate a T matrix (transition matrix) database of “straws” with various lengths, thicknesses, orientations, curvatures, and dielectric properties; we then construct the grass with a group of straws from the database and apply GIEBCM again to calculate the T matrix of the overall grass scene. The grass T matrix is transferred to S matrix (scattering matrix) and combined with the ground S matrix, which is computed using the stabilized extended boundary condition method, to obtain the total scattering. In this article, we will demonstrate the capability of the model by simulating scattering from scenes with different grass densities, different grass structures, different grass water contents, and different ground moisture contents. This model will help with radar experiment design and image interpretation for marshland and wetland observations.
Non-linear structure formation in models of Dark Energy and Modified Gravity

In this thesis we investigate the importance of non-linear structure formation on the determination of cosmological parameters for models beyond the standard ΛCDM scenario. Future galaxy surveys will be able to determine the two-point correlation function of more than 10^7 galaxies in a very precise way. Extracting the information encoded in this function at small scales, allows us to constrain the underlying cosmological model. Here we consider different models beyond ΛCDM: Coupled Dark Energy, Growing Neutrino Quintessence, Effective Field Theory, Horndeski theory and general phenomenological parameterizations of Modified Gravity. To study the non-linear effects in these models, we use different methodologies: fitting formulae, semi-analytic prescriptions based on the Halo model, resummation methods in cosmological perturbation theory and specialized N-body simulations. In order to forecast the constraining power of next-generation surveys, such as Euclid, SKA and DESI, we developed a code that uses the Bayesian Fisher matrix for- malism to compute the inferred error on the parameters for Galaxy Clustering and Weak Lensing observables. For Modified Gravity we study the effect that different parameterizations and different non-linear prescriptions have on the Fisher forecasts. We obtain the best combination of redshift-binned parameters which will be measured by future experiments. Within the Coupled Dark Energy scenario we perform the first forecasts using fitting formulae from N-body simulations and show that using non-linear scales improves the constraints by more than an order of magnitude compared to linear forecasts. In the Growing Neutrino Quintessence model, we use non-Newtonian N-body simulations to study the different regimes for the dynamics of neutrino lumps. We show that there are viable cosmologies within this model and that the lump interactions induce a heating of the neutrino fluid. Finally, we also study analytically the non-linear corrections to the power spectrum in the case of Horndeski theory under the quasistatic approximation. To achieve this, we apply a resummation method for higher order perturbation theory, which had so far only been employed within the standard cosmological model. The main message of this thesis is that non-linear structure formation has a significant impact on cosmological parameter estimation. In order to discriminate between competing theories, using forthcoming data, we need to take non-linearities and their implications into account.
Novel gas holdup and regime transition correlation for two-phase bubble columns

The gas holdup is dimensionless parameter of fundamental and practical importance in the operation, design and scale-up of bubble columns. Unfortunately, the many relationships between the bubble column fluid dynamic parameters and the various variables characterizing the system make it difficult to find general correlations for the precise estimation of the gas holdup. Wilkinson et al. (1992), in their pioneering paper, proposed a correlation to predict the gas holdup in industrial-scale bubble columns, based on the physical properties of the phases and the operating conditions. However, this correlation lacks in generality, as it does not take in account the bubble column design. In this paper, we propose a generalization of the Wilkinson et al. (1992) gas holdup correlation to take into also the bubble column design parameters. Starting from considerations concerning the flow regime transition, corrective parameters are included to account for the effects introduced by the gas sparger openings, the bubble column aspect ratio and the bubble column diameter. The proposed correlation has been found to predict fairly well previously published gas holdup and flow regime transition data.
One-class classifiers ensemble based anomaly detection scheme for process control systems

This paper focuses on the issue of anomaly detection for data in process control systems (PCSs). Considering data features in PCSs, this paper proposes to utilize the notion of one-class classification (OCC). In order to provide a general solution for more types of systems, ensemble learning is combined with OCC models. Two different ensembles of OCC models are proposed based on different scenarios in the process of detection. Performance of the proposed detection scheme is validated via several UCI datasets and two practical PCSs.
Proepileptic patterns in EEG of WAG/Rij rats

In this paper we study specific oscillatory patterns on EEG signals of WAG/Rij rats. These patterns are known as proepileptic because they occur in time period during the development of absence-epilepsy before fully-formed epileptic seizures. In the paper we analyze EEG signals of WAG/Rij rats with continuous wavelet transform and empirical mode decomposition in order to find particular features of epileptic spike-wave discharges and nonepileptic sleep spindles. Then we introduce proepileptic activity as patterns that combine features of epileptic and non-epileptic activity. We analyze proepileptic activity in order to specify its features and time-frequency structure.
Curve reconstruction based on the relative neighbourhood graph.

This thesis work deals with the problem of curve reconstruction in a novel way. Here the relative neighbourhood graph on a set of points is used and a heuristic for curve reconstruction is proposed. Given a sample point set S, first a Relative neighbourhood graph is constructed on S, which is proved to contain all edges joining adjacent points along the unknown curve. Next, the non-adjacent edges are removed to find the reconstruction. Experimental results for sample points drawn from a variety of curves is provided. A second heuristic dealing with curve reconstruction in presence of noise is proposed. Noise points are unwanted points that creep in during data collection. The second heuristic takes a noisy sample as input and produces a filtered set of points to which the first heuristic can be applied to find the reconstruction.
Developing Infographics to Present Research Findings from CBPR to Latinx Farmworker Community Members

Abstract:Background: Community-based participatory research (CBPR) facilitates vulnerable communities and scientists collaborating to address pertinent health issues. For Latinx farmworkers, the employment of children and their resulting morbidity and mortality in the hazardous farm environment is a concern. Communicating child farmworker research results to farmworkers and service providers must take into account their language, literacy, and educational characteristics.Objectives: We describe the collaborative development and dissemination of research findings on child farmworkers by a CBPR partnership with the Latinx farmworker community.Methods: Key points for communication with infographics were abstracted from peer-reviewed research papers. An iterative process sought community partners’ input as the research partners developed the infographics.Lessons Learned: We developed infographics on heat-related illness, education, and musculoskeletal impacts of child labor, guided by published criteria for effective infographics. Efforts to disseminate finished infographics needed greater rigor.Conclusions: Infographics provide a means to communicate CBPR findings to community members.
Virtual reality as a new trend in mechanical and electrical engineering education

Abstract In their daily practice, academics frequently face lack of access to modern equipment and devices, which are currently in use on the market. Moreover, many students have problems with understanding issues connected to mechanical and electrical engineering due to the complexity, necessity of abstract thinking and the fact that those concepts are not fully tangible. Many studies indicate that virtual reality can be successfully used as a training tool in various domains, such as development, health-care, the military or school education. In this paper, an interactive training strategy for mechanical and electrical engineering education shall be proposed. The prototype of the software consists of a simple interface, meaning it is easy for comprehension and use. Additionally, the main part of the prototype allows the user to virtually manipulate a 3D object that should be analyzed and studied. Initial studies indicate that the use of virtual reality can contribute to improving the quality and efficiency of higher education, as well as qualifications, competencies and the skills of graduates, and increase their competitiveness in the labour market.