Miracle-dz/UniFAR-FTUs huggingface dataset:
The signed Euclidean distance transform and its applications

The signed Euclidean distance transform described is a modified version of P.E. Danielsson's Euclidean distance transform (1980). The distance transform produces a distance map in which each pixel is a vector of two integer components. If a distance map is created inside the objects, the two integer values of a pixel in the distance map represent the displacements of the pixel from the nearest background point in the x and y directions, respectively. The unique feature of this distance transform, that a vector in the distance map is always pointing to the nearest background point, is exploited in several applications, such as the detection of dominant point in digital curves, curve smoothing, computing Dirichlet tessellations and finding convex hulls. <<ETX>>
Two-Stage Channel Estimation Using Convolutional Neural Networks for IRS-Assisted mmWave Systems

Intelligent reflecting surface (IRS) is expected to be an essential component of next-generation wireless communication networks due to its potential to provide similar or higher array gain with lower hardware cost and energy consumption compared with massive multiple-input--multiple-output (MIMO) technology. However, channel estimation in IRS-assisted communication systems is more challenging than that in conventional systems. In this article, we propose a two-stage channel estimation approach using deep learning in millimeter-wave (mmWave) communication systems with a hybrid passive/active IRS structure. In the first stage, the sparsity of the mmWave massive MIMO channel in the angular domain is exploited to estimate the amplitude of the sparse channel through a convolutional neural network. In this way, the indices of the nonzero entries of the sparse channel can be simultaneously obtained. In the second stage, the channel is reconstructed by solving a least squares problem with the acquired indices. The simulation results show that the proposed channel estimation scheme could achieve better performance with manageable complexity over the existing solutions.
A self-adaptive edge matching method based on mean shift and its application in video tracking

Abstract A self-adaptive edge matching method based on mean shift adjustment is proposed in this paper. Such method uses the local mode seeking character of mean shift to adjust the edge information of each model to a stable state before matching, which can effectively avoid the deviation problem of traditional method and raise the successful matching rate. Furthermore, the interfering vector with a self-adaptive coefficient is proposed to optimise the matching performance in complex background. Compared with a pre-set constant coefficient, the self-adapted coefficient has a better perception of background edge complexity so as to control the initial adjusting position more rationally, and thus increases the robustness and accuracy of matching. This matching method is applied in an improved particle filtering tracking framework, and experimental results prove the validity and rationality of the theoretical analysis, and show that the proposed matching method performs a robust and efficient tracking.
A SPECTRAL CLASSIFICATION OF GALAXIES

There is at least one advantage in a classification of galaxies based on their spectra : the absorption and emission features, despite their composite nature, contain significant information bearing on the stellar populations of the systems. Such a classification, however, should be regarded as complementary to the classical Hubble sequence based on form and degree of resolution. The reason is that the spectral characteristics considered here seem to correlate rather well with the appearance of spiral structure and central concentration, which are two properties of galaxies that show progressive changes in Hubble's scheme. Thus the classification proposed here will incorporate additional information that did not, in general, enter into Hubble’s assignments of nebular types. The procedure of classification was very simple : accurate estimates of spectral type (and of luminosity class, when possible) were made for a number of the brightest galaxies. Special attention was given to the wavelength region used and to the particular parts of the galaxies to which the spectra referred. The observational material consists of spectrograms obtained principally by Humason at the Mount Wilson and Palomar Observatories and by Mayall at the Lick Observatory. For some objects new plates were taken with the nebular spectrograph of the 36-inch Crossley
The Galaxy Evolution Explorer

The Galaxy Evolution Explorer (GALEX), a NASA Small Explorer Mission planned for launch in Fall 2002, will perform the first Space Ultraviolet sky survey. Five imaging surveys in each of two bands (1350-1750Å and 1750-2800Å) will range from an all-sky survey (limit mAB~20-21) to an ultra-deep survey of 4 square degrees (limit mAB~26). Three spectroscopic grism surveys (R=100-300) will be performed with various depths (mAB~20-25) and sky coverage (100 to 2 square degrees) over the 1350-2800Å band. The instrument includes a 50 cm modified Ritchey-Chrétien telescope, a dichroic beam splitter and astigmatism corrector, two large sealed tube microchannel plate detectors to simultaneously cover the two bands and the 1.2 degree field of view. A rotating wheel provides either imaging or grism spectroscopy with transmitting optics. We will use the measured UV properties of local galaxies, along with corollary observations, to calibrate the UV-global star formation rate relationship in galaxies. We will apply this calibration to distant galaxies discovered in the deep imaging and spectroscopic surveys to map the history of star formation in the universe over the red shift range zero to two. The GALEX mission will include an Associate Investigator program for additional observations and supporting data analysis. This will support a wide variety of investigations made possible by the first UV sky survey.
Role of Clustering Coefficient on Cooperation Dynamics in Homogeneous Networks

Based on previous works, we give further investigations on the Prisoners' Dilemma Game (PDG) on two different types of homogeneous networks, i.e. the homogeneous small-world network (HSWN) and the regular ring graph. We find that the so-called resonance-like character can occur on both the networks. Different from the viewpoint in previous publications, we think the small-world effect may be unnecessary to produce this character. Therefore, over these two types of networks, we suggest a common understanding in the viewpoint of clustering coefficient. Detailed simulation results can sustain our viewpoint quite well. Furthermore, we investigate the Snowdrift Game (SG) on the same networks. The difference between the outputs of the PDG and the SG can also sustain our viewpoint.
Efficient Computation of the Max-Plus Semantics of Synchronous Dataflow Graphs

Streaming systems are naturally modeled with synchronous dataflow graphs (SDFGs). The max-plus semantics of an SDFG is a compact matrix representation of its timing behavior. The max-plus semantics enables us to analyze and control timing properties of the systems, such as the obtainable minimum guaranteed throughput and maximum latency. Deriving the max-plus semantics, and consequently, performance analysis may be computationally expensive since the state-of-the-art method simulates one iteration of an SDFG. This holds, in particular, for systems whose components operate at different levels of granularity, as this results in many executions of some components in one iteration. This article aims at efficiently calculating the max-plus semantics of SDFGs. This article proposes an optimization framework exploring decompositions of a given SDFG and finding a composition sequence whose computational effort for compositionally obtaining the max-plus semantics is minimal. Not only does our proposed technique accelerate the performance analysis of multiscale streaming systems, but it also allows us to compute the max-plus semantics of some systems for which the state-of-the-art method does not succeed because of memory limitations.
Reliability of bottom-gate graphene field-effect transistors prepared by using inductively coupled plasma-chemical vapor deposition

Stability degradation in bottom-gate graphene field-effect transistors prepared by using inductively coupled plasma-chemical vapor deposition method are investigated under bias, temperature and illumination stress. The stretched-exponential time dependence model, which can be derived based on the trapping/detrapping of charges to/from existing traps and continuous redistribution of charges in bulk dielectrics, is well applied in fitting the time dependence of the transfer curve shifts in all stress conditions. The stress wavelength is considered as important factors as well as the bias and temperature stress in the transfer characteristic instabilities of graphene FEFs.
Automated Flexible Modeling for Various Full-SiC Power Modules

Various types of SiC power module product have been developed in recent years. Therefore, the device model developing speed and the flexibility corresponding to each product are further required. In this article, a novel high-accuracy data-driven modeling method was developed using 1.2-, 3.3-, and 6.5-kV full-SiC power module measurement data. A static model was rapidly developed based on an automated approach using an optimization algorithm instead of the general model parameter extracting method by using a fixed model equation. Furthermore, the simulated annealing method was used to minimize the objective function that is calculated based on the difference in dynamic characteristics by optimizing adjustment parameters to reduce the error. The parameter optimization method focuses on not only <italic>I</italic><sub>D</sub> and <italic>V</italic><sub>DS</sub> but also <italic>I</italic><sub>G</sub> and <italic>V</italic><sub>GS</sub> waveforms. As a result, the relative errors of di/dt, dv/dt, and switching loss in the wide current region decreased considerably while maintaining the consistency of the <italic>I</italic><sub>G</sub> and <italic>V</italic><sub>GS</sub> waveforms. The modeling method can be used to realize numerous full-SiC power module models easily. The proposed algorithm provides fast, automated, and high-accuracy modeling. Furthermore, the optimization of the model can facilitate considerable expansion of the full-SiC power module usage.
Self-adaptive road tracking in hyperspectral data for C-IED

For Counter Improvised Explosive Devices purposes, main routes including their vicinity are surveyed. In future military operations, small hyperspectral sensors will be used for ground covering reconnaissance, complementing images from infrared and high resolution sensors. They will be mounted on unmanned airborne vehicles and are used for on-line monitoring of convoy routes. Depending of the proximity to the road, different regions can be defined for threat assessment. Automatic road tracking can help choosing the correct areas of interest. Often, the exact discrimination between road and surroundings fails in conventional methods due to low contrast in pan-chromatic images at the road boundaries or occlusions. In this contribution, a novel real-time lock-on road tracking algorithm is introduced. It uses hyperspectral data and is specifically designed to address the afore- mentioned deficiencies of conventional methods. Local features are calculated from the high-resolution spectral signatures. They describe the similarity to the actual road cover and to either roadside. Classification is per- formed to discriminate the signatures. To improve robustness against variations in road cover, the classification results are used to progressively adapt the road and roadside classes. Occlusions are treated by predicting the course of the road and comparing the signatures in the target area to previously determined road cover signa- tures. The algorithm can be easily extended to show regions of varying threat, depending on the distance to the road. Thus, complex anomaly detectors and classification algorithms can be applied to a reduced data set. First experiments were performed for AISA Eagle II (400nm - 970nm) and AISA Hawk (970nm - 2450nm) data
Independent Pixel and Monte Carlo Estimates of Stratocumulus Albedo

Abstract Monte Carlo radiative transfer methods are employed here to estimate the plane-parallel albedo bias for marine stratocumulus clouds. This is the bias in estimates of the mesoscale-average albedo, which arises from the assumption that cloud liquid water is uniformly distributed. The authors compare such estimates with those based on a more realistic distribution generated from a fractal model of marine stratocumulus clouds belonging to the class of “bounded cascade” models. In this model the cloud top and base are fixed, so that all variations in cloud shape are ignored. The model generates random variations in liquid water along a single horizontal direction, forming fractal cloud streets while conserving the total liquid water in the cloud field. The model reproduces the mean, variance, and skewness of the vertically integrated cloud liquid water, as well as its observed wavenumber spectrum, which is approximately a power law. The Monte Carlo method keeps track of the three-dimensional paths sol.
Effect of E-Service Quality on E-Repurchase Intention in Indonesia Online Shopping: E-Satisfaction and E-Trust as Mediation Variables

The purpose of this study is to develop new knowledge in understanding the effect of electronic service quality on customer satisfaction, customer trust and customer purchase intention and to understand the mediating role of customer satisfaction and customer trust in the relationship between service quality and purchase intention in online shopping in Indonesia. The result is expected to be a better strategy in developing service quality electronically which has an impact on customer behavior. This study uses a sample of 264 online consumers in Indonesia. Analysis data using SmartPLS 4 software shows that the quality of e-service has a positive and significant effect on e-satisfaction, e-trust and e-repurchase intention. E-satisfaction and e-trust are also able to mediate the relationship between e-service quality and e-repurchase intention. In addition, the study also found that e-satisfaction has a significant and positive effect on e-trust. Overall the quality of electronic services is statistically significant and positive relationship with customer behavior. Future research is expected to be able to consider various product segments and other types of industries to ensure that the measurement works well.
The Unlocking Process Leading to the 2016 Central Italy Seismic Sequence

Approximately 23,000 well‐located earthquakes from 2009 to 2016 are used as templates to recover seismic activity preceding the 2016 Central Italy seismic sequence. The resulting spatiotemporal pattern is analyzed by additional ∼91,000 newly detected events. In the 8 years before the sequence onset, seismicity (ML ≤ 3.7) develops at the hangingwall of the 2016 normal faults and along a sub‐horizontal shear zone, bounding the active extensional system at depth. This activity, mainly organized in foreshock‐mainshock and swarm‐like clusters, migrates toward the nucleation area of the first Mw 6.0 mainshock of the sequence (24th of August in Amatrice). We propose an unlocking process based on variable temporal clustering of the seismicity, including repeaters, identifying fault portions with different degrees of coupling. Such a progressive localization of the seismic activity at the fault edges induces a weakening of the locked patch of the Amatrice mainshock.
Pertussis epidemiology in Canada, 2005–2019

Background Pertussis, also known as whooping cough, is an endemic vaccine-preventable disease that affects the respiratory tract and is caused by the bacterium Bordetella pertussis. Between 1999 and 2004, the adolescent booster dose of pertussis was introduced across Canada. This report describes the epidemiology of pertussis in Canada from 2005 to 2019, the period after adolescent acellular vaccination was recommended. Methods We analyzed pertussis incidence by year, age groups, sex and geographic region using national surveillance data from the Canadian Notifiable Disease Surveillance System. Hospitalization data from the Discharge Abstract Database was used to investigate pertussis hospitalizations by sex and age. Deaths from pertussis were explored using Statistics Canada’s vital statistics data. Vaccination coverage data was gathered from the 2019 Childhood National Immunization Coverage Survey and 2018–2019 Seasonal Influenza Vaccination Coverage Survey. Results Between 2005 and 2019, there were a total of 33,481 pertussis cases with the average annual incidence rate of 6.4 cases per 100,000 population. The highest average age-specific incidence rate was among infants under one year of age (n=68.7 cases per 100,000 population). There were a total of 1,593 pertussis hospitalizations; nearly 80% of these hospitalizations were infants under one year of age. Hospitalization rates were 8.2 times higher in infants three months or younger compared to infants four to 11 months of age. There were 17 deaths; all among infants under one year of age. Conclusion The highest morbidity and fatality of pertussis were among infants under one year of age. It is important to take measures to reduce transmission to infants who are too young to be vaccinated. Increasing vaccine coverage in children and pregnant women are important to reduce the burden of disease.
Observation of alpha particle slowing-down spectra in JET helium beam fuelling and heating experiments

The first experimental results are reported of anisotropic slowing-down features observed in JET helium beam fuelling experiments. Two independent observation ports, one with a view perpendicular to the magnetic field in the centre of the plasma and a second multichord viewing arrangement, approximately tangential to the toroidal field, provide radially and temporally resolved information on the velocity distribution function comprising the populations of both fast and thermalized alpha particles. The fuelling process is characterized by a change-over from a distinctly non-Maxwellian distribution function to a dominantly Maxwellian distribution and also by a broadening of the deduced fast ion density radial profile. The fast particle component in the observed composite charge exchange spectrum is found to be in excellent agreement with predictions are based on anisotropic velocity distribution functions obtained from the analytical solution of the neutral injection Fokker-Planck equation. Signal-to-noise levels in the measurement of fast alpha particle in the JET helium fuelling campaign are extrapolated to thermonuclear-fusion alpha particle density levels expected for the D-T phase of JET. It is shown that beam penetration and not competing continuum radiation is a major constraint, and that acceptable (hydrogen or helium) neutral beam power and energy requirements promise a feasible CX alpha particle diagnosis in the core of next-step devices such as ITER.
Mechanical Properties of AISI 316L Lattice Structures via Laser Powder Bed Fusion as a Function of Unit Cell Features

The growth of additive manufacturing processes has enabled the production of complex and smart structures. These fabrication techniques have led research efforts to focus on the application of cellular materials, which are known for their thermal and mechanical benefits. Herein, we studied the mechanical behavior of stainless-steel (AISI 316L) lattice structures both experimentally and computationally. The lattice architectures were body-centered cubic, hexagonal vertex centroid, and tetrahedron in two cell sizes and at two different rotation angles. A preliminary computational study assessed the deformation behavior of porous cylindrical samples under compression. After the simulation results, selected samples were manufactured via laser powder bed fusion. The results showed the effects of the pore architecture, unit cell size, and orientation on the reduction in the mechanical properties. The relative densities between 23% and 69% showed a decrease in the bulk material stiffness up to 93%. Furthermore, the different rotation angles resulted in a similar porosity level but different stiffnesses. The simulation analysis and experimental results indicate that the variation in the strut position with respect to the force affected the deformation mechanism. The tetrahedron unit cell showed the smallest variation in the elastic modulus and off-axis displacements due to the cell orientation. This study collected computational and experimental data for tuning the mechanical properties of lattice structures by changing the geometry, size, and orientation of the unit cell.
Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.
An adaptive image restoration algorithm based on hybrid total variation regularization

: In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present. Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving. However, TV regularization sometimes causes staircase artifacts with piecewise constants. To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation. We study the existence and uniqueness of the minimizer for the considered model. Numerically, the minimization problem can be eﬀiciently solved by the alternating minimization method. Furthermore, we give rigorous convergence analyses of our algorithm. Experiments results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for deblurring and denoising images simultaneously, in comparison with several state-of-the-art numerical algorithms.