Miracle-dz/UniFAR-FTUs huggingface dataset:
Line profile variations caused by low-frequency nonradial pulsations of rapidly rotating stars

Line profile variations caused by low-frequency nonradial g-mode oscillations of a rotating star are presented. For a nonradial oscillation mode whose oscillation frequency in the corotating frame is comparable to or smaller than the rotation frequency, the latitudinal dependence of the oscillation amplitude deviates significantly from that for a nonrotating star. Rotation usually causes oscillations confined to a narrow equatorial belt as well as causing toroidal velocity fields. The concentration of the amplitude of the oscillation toward the equator leads to a reduction in the strength of the bumps in line profiles if the maximum velocity of nonradial pulsation is fixed. For the same normalization, the line profile variations due to the sectoral prograde mode are most visible, while those caused by retrograde waves are almost invisible due to a cancellation effect. The toroidal component generated by stellar rotation appreciably affects the line profile variation of some modes. 24 refs.
Uncertainty Quantification for Sparse Estimation of Spectral Lines

Line spectral estimation is an important problem that finds many useful applications in signal processing. Many high-performance methods have been proposed for solving this problem: they select the number of spectral lines and provide point estimates of the frequencies and amplitudes of such spectral lines. This paper studies the line spectral estimation problem from a different and equally important angle: uncertainty quantification. More precisely, this paper develops a novel method that provides an uncertainty measure for the number of spectral lines and also offers point estimates and confidence intervals for other parameters of interest. The proposed method is based on the generalized fiducial inference framework and is shown to possess desirable theoretical and empirical properties. It has also been numerically compared with existing methods in the literature and applied for the detection of exoplanets.
GCA-Net: Geometrical Constraints-based Advanced Network for Polyp Segmentation

Colonoscopy is a common procedure for detecting and screening colorectal polyps, however, it poses a number of challenges because of the properties of images and the features of polyps inside, which can vary in shape, color, condition, and clear non-distinction with the surrounding context. Deep learning-based approaches have recently emerged, which considerably boost polyp segmentation procedures and gradually convert and replace out-of-date methods. Nevertheless, determining an effective measure remains a significant difficulty, which motivates us to propose GCA-Net for polyp segmentation. We first employ EfficientNetV2 as the backbone to extract improved salient features, then pass them via two modules, SE-PASPP and SE-RFB, to capture more contexture before feeding them into a dual-asymmetrical partial decoder to build a final resolution map. Finally, we develop a novel loss function based on the region-based strength of Dice loss and the geometrical constraints-based advantages of Active Contour with Elastica (ACE) loss. The suggested method's preeminence is demonstrated by comparing results to over state-of-the-art approaches in both learning and generalization assessment.
A Universal Lossless Compression Method Applicable to Sparse Graphs and Heavy–Tailed Sparse Graphs

Graphical data arises naturally in several modern applications, including but not limited to internet graphs, social networks, genomics and proteomics. The typically large size of graphical data argues for the importance of designing universal compression methods for such data. In most applications, the graphical data is sparse, meaning that the number of edges in the graph scales more slowly than <inline-formula> <tex-math notation="LaTeX">$n^{2}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denotes the number of vertices. Although in some applications the number of edges scales linearly with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, in others the number of edges is much smaller than <inline-formula> <tex-math notation="LaTeX">$n^{2}$ </tex-math></inline-formula> but appears to scale superlinearly with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We call the former sparse graphs and the latter heavy-tailed sparse graphs. In this paper we introduce a universal lossless compression method which is simultaneously applicable to both classes. We do this by employing the local weak convergence framework for sparse graphs and the sparse graphon framework for heavy-tailed sparse graphs.
Calibration of Automatic Seizure Detection Algorithms

An EEG seizure detection algorithm employed in a clinical setting is likely to encounter many EEG segments that are difficult to classify due to the complexity of EEG signals and small data sets frequently used to train seizure detectors. The detectors should therefore be able to notify the clinician when they are uncertain in their predictions and they should also be accurate for confident predictions. This would enable the clinician to focus mainly on the parts of the recording where confidence in predictions is low. Here we analyse the calibration of neonatal and adult seizure detection algorithms based on a convolutional neural network in terms of how well the output seizure/non-seizure probabilities estimate the corresponding empirical frequencies. We found that the detectors turned out to be overconfident, in particular when incorrectly predicting seizure segments as non-seizure segments. The calibration of both detectors, measured in terms of expected calibration error and overconfidence error, was improved noticeably with the use of Monte Carlo dropout. We find that a straightforward application of dropout during training and classification leads to a noticeable improvement in the calibration of EEG seizure detectors based on a convolutional neural network.
VLBI Observations of Symmetric Parsec-Scale Twin Jets in the Narrow-Angle-Tail Radio Galaxy NGC 1265 (3C 83.1B)

We present global VLBI observations of the nucleus of the narrow-angle–tail radio galaxy NGC 1265. We report the discovery of symmetric parsec-scale twin jets which are well aligned with the kiloparsec-scale jets. We also present an archival Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC2) image of the galaxy, showing an apparently almost edge-on dust disk within 10° of perpendicular to the jet axis. The jets in NGC 1265 are consistent with either (1) relativistic motion near the plane of the sky, (2) nonrelativistic motion, or (3) a "coaxial" structure with a nonrelativistic sheath surrounding a relativistic spine. A study of the properties of the nine large-scale sources known so far with twin parsec-scale jets suggest that these sources are more likely to be nonrelativistic or only mildly relativistic. We also suggest that at least some sources with symmetric parsec-scale jets have been decelerated on the parsec scale, possibly through interaction with ambient gas or the shock at the sonic radius of a cooling flow. Other sources may be reborn compact symmetric object-type sources propagating out of the galaxy's nuclear interstellar medium. Further proper-motion studies of this class of source are needed.
Analyzing Sensitive Factors Affecting Online Academic Performance in the New Normal: A Machine Learning Perspective

Online distance learning (ODL) is one extension of the distance learning approach introduced by the Department of Education (DepEd) as part of its learning continuity in the new normal (COVID - 19 times). Despite the advantages brought by online learning in continuing learners' learning experiences and improving learners' academic performance during the pandemic, it is still of vital importance to examine what factors are sensitive to changes in learner's online academic performance. In this study, sensitive factors affecting online academic performance are examined through the lens of machine learning (ML) methods: Boruta algorithm (BA) for feature selection; multilayer perceptron neural network (MLP NN) for model formulation; and partial derivatives method (PDM) for sensitivity analysis. Data used in the analysis are responses in the survey participated by N = 978 senior high and junior school students of a private high school institution in the Philippines. Out of eighteen factors considered in the analysis, BA revealed only six relevant factors that contributes greater information to changes in student's online academic performance. Formulated MLP NN model achieved a high testing accuracy of 0.932 with a kappa coefficient of 0.891 and an f - measure of 0.924, that aided the sensitivity analysis using PDM to have better results. Sensitivity analysis showed that motivation and mental well- being are the most sensitive factors affecting both below average and above average online academic performance.
Deploying Use Case Specific Network Slices Using An OSM Automation Platform

Network slicing is a novel fifth generation (5G) concept that enables operators to create logical networks (slices), each dedicated to certain specific applications, on top of a common network infrastructure, computation and storage resources. These slices are logically isolated from each other with respect to network performance and security, providing services to vertical industries and various use cases. Accordingly, an orchestrator is needed in order to manage network resources for various slices. Among different orchestrators that have been developed, open source MANO (OSM) appears as a popular platform, providing a software stack for slice developers. In this paper, we present an implementation of deploying network slices based on the OSM automation platform and report preliminary experiments that have been performed. To validate our implementation, a specific media streaming use case has been selected and a slice with three pre-configured network services that contain a media server has been created.
Factors Influencing Indonesian Consumers to Use Digital Banking

In Indonesia, digital banking has expanded and expanded rapidly. According to the central bank, Bank Indonesia, the volume of electronic money transactions has climbed by 41,35% and the value of digital banking transactions increased by 46,53% year-on-year in February 2022. Previous study in Indonesia has mostly focused on the use of internet and mobile banking services provided by conventional banks. This purpose of this study is to investigate the factors of features, convenience of use, security, trust, and social influence on decisions to utilize digital banking. In contrast the variable of intention is employed as mediator. This study includes all digital banking customers in Indonesia who live in Jakarta. The sample in this study was 215 people. Structural Equation Modeling (SEM) analysis with Smart Partial Least Square (PLS) 3.0 software was employed in this study. The results of this study indicate that the features, security, and ease of use variables have a positive and significant effect on the intention to use digital banking services. Similarly, the variable of intention to use has a favorable and significant influence on decisions to use digital banking.
A Security-Enabled Safety Assurance Framework for IoT-Based Smart Homes

The exponential growth of the Internet of Things (IoT) has paved the way for safety-critical cyber-physical systems to enter our everyday activities. While such systems have changed the way of our life, they brought new challenges that can adversely affect our life and the environment. Safety and security are two such challenges that can hamper the widespread adoption of new IoT applications. Due to a large number of connected devices and their ability to control critical physical assets, intended attacks on them and/or unintended failure events such as mechanical failure of devices, communication failure, and unforeseen bad interactions between connected devices may cause an IoT-based system to enter into unsafe and dangerous physical states. By considering the importance of safety and security of IoT systems, in this article, we present a security-enabled safety monitoring framework for IoT-based systems. In the proposed framework, we utilize design-time system analysis to create an executable monitoring model that enables run-time safety assurance provision for a system via collecting and analyzing operational data and evidence to determine the safety status of the system and then taking appropriate actions and securely communicating the safety status and recommended actions to the system users to minimize the risk of the system entering into an unsafe state.
Dynamics of sub-hyperbolic and semi-hyperbolic rational semigroups and skew products

We consider dynamics of sub-hyperbolic and semi-hyperbolic semigroups of rational functions on the Riemann sphere and will show some no wandering domain theorems. The Julia set of a rational semigroup in general may have non-empty interior points. We give a sufficient condition that the Julia set has no interior points. From some information about forward and backward dynamics of the semigroup, we consider when the area of the Julia set is equal to zero or an upper estimate of the Hausdorff dimension of the Julia set.
Complex Correlated Phase Gradient Variance Based Optical Coherence Tomography Angiography

In this study, we reported a complex-signal-based optical coherence tomography angiography (OCTA) method, called complex correlated phase gradient variance (CCPGV), for mapping high-quality microvascular images. The performance of the newly proposed algorithm is benchmarked against the previously reported phase-resolved Doppler variance, complex differential variance (CDV), and split-spectrum amplitude and phase-gradient angiography (SSAPGA), by both tissue phantoms experiments and in vivo human skin measurements. Compared to the phase-resolved Doppler variance, CCPGV can intrinsically reject undesirable phase shifts caused by bulk motion and trigger jitter. In contrast to CDV and SSAPGA, which are insensitive to phase instability, CCPGV can provide superior motion contrast, as demonstrated by ∼1.4 and 3 times higher contrast in phantom experiments respectively. An increase of 27.3% and 106.3% were found in vivo experiments for CCPGV, making it easier to distinguish vessels from the background static. Benefiting from the advantages, more vessels and better connectivity can be visualized in the en-face angiogram processed by the CCPGV method. We believe that our method will benefit the biomedical community to some extent in disease diagnosis and monitoring.
Boost Packed E-Cell: A Compact Multilevel Converter for Power Quality Ancillary Services

This paper proposes Boost Packed E-Cell (BPEC) as an affordable Compact Multilevel Converter (CMLC) for the power quality ancillary services. The BPEC is a transformerless bidirectional CMLC topology and can generate symmetrical and asymmetrical multilevel voltage waveforms with 5-to-11-level resolution using only three low-voltage dc capacitors and eight power switches. Thanks to the serial expansion of the two dc capacitors, BPEC has two dc-links, which means two voltage sensors are enough to control three dc capacitors. Despite other CMLCs, BPEC does not need a fault detector and inherently generates a symmetrical 5-level or an asymmetrical 7-level voltage waveform in the event of a fault of the middle switches or their gate drivers. As a case study, a single-phase Compact Active Power Filter (CAPF) is designed in this paper based on the BPEC to compensate for harmonics and reactive power caused by unknown non/linear loads, simultaneously. Finite control set predictive control strategy is also adopted based on the switched model of the power system containing the grid, unknown non/linear loads, and the BPEC to address the grid power quality concerns. The experiments performed by a prototype including the BPEC power board, MicroLabBox, OPAL-RT OP8662, and Chroma 61086 verify the merits of the proposed CAPF in practice.
Errors in fixed and moving frame of references - Applications for conventional and Doppler radar analysis

Abstract Procedures for the estimation and correction of advection effects with single and multiple conventional and Doppler radars are developed. In the case of scalars or Cartesian vectors, the essence of the method is finding a moving frame of reference where (in the least-square sense) the observations are as stationary as possible. It is shown that this is quite different from the more traditional correlation techniques. In the case of non-Cartesian vectors (e.g., radial velocities from Doppler radars), very different criteria are derived. For a single Doppler radar, we look for a frame of reference where ∫[∂2/∂t2(vrr)]2 is a minimum. Here vr is the radial velocity, t is time and r is the distance of the observed point from the radar. In the multiple Doppler case, it is shown that in a frame of reference moving with the advection speed, ∫[∂/∂t(vr(1)r(1)) − ∂/∂t(vr(2)r(2))]2is a minimum. Here superscripts (1) and (2) refer to radars (1) and (2), respectively. For scalars (such as radar reflectivities).
Fixed Effects and Bias Due to the Incidental Parameters Problem in the Tobit Model

Abstract The maximum likelihood estimator (MLE) in nonlinear panel data models with fixed effects is widely understood (with a few exceptions) to be biased and inconsistent when T, the length of the panel, is small and fixed. However, there is surprisingly little theoretical or empirical evidence on the behavior of the estimator on which to base this conclusion. The received studies have focused almost exclusively on coefficient estimation in two binary choice models, the probit and logit models. In this note, we use Monte Carlo methods to examine the behavior of the MLE of the fixed effects tobit model. We find that the estimator's behavior is quite unlike that of the estimators of the binary choice models. Among our findings are that the location coefficients in the tobit model, unlike those in the probit and logit models, are unaffected by the “incidental parameters problem. ” But, a surprising result related to the disturbance variance emerges instead – the finite sample bias appears here rather than in the slopes. This has implications for estimation of marginal effects and asymptotic standard errors, which are also examined in this paper. The effects are also examined for the probit and truncated regression models, extending the range of received results in the first of these beyond the widely cited biases in the coefficient estimators.
Mixing enhancement via the release of strongly nonlinear longitudinal Görtler vortices and their secondary instabilities into the mixing region

Mixing enhancement in a mixing layer is considered in terms of a ‘vortex generator’ that uses fluid dynamically generated counter-rotating longitudinal vortices rather than explicit winglets or similar devices. This view is reached through considering the centrifugal instability of weak initial Görtler vortices on a slightly concave wall that are allowed to develop to their various nonlinear stages through selecting the cutoff lengths of the trailing edge prior to their release into the mixing region. These vortices are released from one side of the (say, upper) stream in the present work. The quantitative entrainment properties of the longitudinal vortices are studied to select an optimal trailing-edge cutoff for fixed upstream conditions. As the vortices develop along the wall, they are intensified because of the centrifugal instability mechanism and because of the work done by the Reynolds stress of the vortices against the local mean flow rate of strain; simultaneously, the region of strong streamwise vorticity moves away from the wall. This selection process is explained through a balance between the vorticity strength and proximity to the lower stream when the trailing edge is cut off: it is shown, therefore, that vortices of relatively modest strength and kinetic energy that are close to the interface separating the two streams provide mixing properties superior to stronger vortices located too far from the interface. Energy-balancing mechanisms and the stretching of the initial interface are studied, as are the effects of the velocity ratio and the spanwise wavelengths other than the fundamental. In order further to enhance mixing by exploiting the inherent secondary instability of primary steady longitudinal vortices, the most amplified secondary instability of the optimal-trailing-edge cutoff situation, which is the sinuous mode, is studied in detail in terms of the nonlinear development and modification of the steady vortical flow. Local energy-exchange mechanisms are studied, as are the mixing properties of the modified steady flow, which are shown to be significantly improved compared to the unmodified steady flow. Though the initiation of steady longitudinal vortices relies on centrifugal instability upstream, such vortices are able to develop self-sustaining and amplifying properties through the Reynolds stresses in the mixing region even without centrifugal instability reinforcement. The secondary instability is initiated and sustained entirely through its own three-dimensional Reynolds stress properties, which work against the three-dimensional rates of strain in the entire steady flow. This contrasts with initially generated potential-like vortices that decay downstream in the presence of dissipative mechanisms without the production mechanisms due to the Reynolds stresses.
Exploring the INTEGRAL sources in search for new microquasars

Here we present a search for new microquasars among the sources detected with the INTEGRAL satellite (IGR sources). We focus on radio emitting IGR sources and report the discovery of two new probable extragalactic sources behind the galactic plane, as well as the detection at higher energies of the ASCA source AX J1639.0-4642, probably a new microquasar in the Galaxy and coincident with a high-energy gamma-ray emitting EGRET source.
Thermal radiation in Arctic stratus clouds

Infrared radiative properties of Arctic stratus clouds in the 10.1 to 12. 7μm wavelength region are determined from a series of aircraft measurements. The average emissivity of the clouds when scattering is neglected is unity for cloud depths greater than 350m. Under the assumption that the cloud layers are homogeneous the cloud droplet volume absorption coefficient is estimated as 17±5km−1. Several mass absorption coefficients are estimated for several assumed liquid water distributions. It is also concluded that the cloud reflectance is not larger than 2(£5%).
RANDOM GRAPH PROCESSES WITH MAXIMUM DEGREE 2

Suppose that a process begins with n isolated vertices, to which edges are added randomly one by one so that the maximum degree of the induced graph is always at most 2. In a previous article, the authors showed that as n→∞, with probability tending to 1, the result of this process is a graph with n edges. The number of l-cycles in this graph is shown to be asymptotically Poisson l≥3, and other aspects of this random graph model are studied.
Novikov–Poisson Algebras

Abstract We classify Novikov–Poisson algebras whose Novikov algebras are simple with an idempotent element. Moreover, a class of simple Novikov algebras without idempotent elements is constructed through Novikov–Poisson algebras. Certain new simple Lie superalgebras induced by Novikov–Poisson algebras are introduced.