Miracle-dz/UniFAR-FTUs huggingface dataset:
On factoring RSA modulus using random-restart hill-climbing algorithm and Pollard’s rho algorithm

The security of the widely-used RSA public key cryptography algorithm depends on the difficulty of factoring a big integer into two large prime numbers. For many years, the integer factorization problem has been intensively and extensively studied in the field of number theory. As a result, a lot of deterministic algorithms such as Euler’s algorithm, Kraitchik’s, and variants of Pollard’s algorithms have been researched comprehensively. Our study takes a rather uncommon approach: rather than making use of intensive number theories, we attempt to factorize RSA modulus n by using random-restart hill-climbing algorithm, which belongs the class of metaheuristic algorithms. The factorization time of RSA moduli with different lengths is recorded and compared with the factorization time of Pollard’s rho algorithm, which is a deterministic algorithm. Our experimental results indicates that while random-restart hill-climbing algorithm is an acceptable candidate to factorize smaller RSA moduli, the factorization speed is much slower than that of Pollard’s rho algorithm.
Structural Response of Concrete Beams Subjected to Drop Weight Impact - A parametric study using numerical modelling

A structure subjected to an impact load will respond differently compared to when subjected to static loading. Finite element modelling of dynamically loaded structures has not been practiced to the same extent as of statically loaded ones, and consequently the results are not as reliable. Modelling choices in nonlinear finite element analyses of dynamically loaded structures are important to the resulting response of the model. In this thesis different levels of numerical modelling are used to describe the structural behaviour of reinforced concrete beams subjected to drop weight impact. An aim of this project was to develop an already existing 3D solid finite element (FE) model in LS-DYNA. Another objective was to evaluate when simplified models, such as a 2D FE model using beam elements in Abaqus and a two-degrees-of-freedom (2DOF) system, are applicable. Another aim was to investigate the sensitivity of these models as well as the 3D FE model through parametric studies. Based on an existing 3D solid FE model, a more refined model was created by comparing different modelling techniques regarding e.g. structural behaviour of the reinforcement, and interaction between concrete and reinforcement. The 2DOF model, the Abaqus model and the LS-DYNA model were validated by comparison to test results. Using these validated models, parametric studies were performed varying the dynamic load and the beam geometry as well as the amount of reinforcement. The further development of the LS-DYNA model by including a bond-slip relation and a non-linear structural behaviour of the reinforcement gave satisfying results. For the 2DOF model used in this thesis it was found to be necessary that the model is calibrated for each separate case and that the correspondence with the Abaqus model was less accurate when the mass of the drop weight was small in relation to the beam. This indicated that the time dependency of the response of the beam is captured in the Abaqus model, but not in the 2DOF model. To conclude, it is believed that time dependent transformation factors in the 2DOF model would make it possible to capture a behaviour like that in Abaqus and achieve a model that is applicable in all load cases.
C V ] 5 J un 2 01 8 Stochastic Gradient Descent with Hyperbolic-Tangent Decay

Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive method, cosine scheduler and cyclical scheduler. This paper proposes a new scheduling method, named hyperbolic-tangent decay (HTD). We run experiments on several benchmarks such as: ResNet, Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets, LSTM for PAMAP2 dataset, ResNet on ImageNet and Fashion-MNIST datasets. In our experiments, HTD outperforms step decay and cosine scheduler in nearly all cases, while requiring less hyperparameters than step decay, and more flexible than cosine scheduler.
Adaptive neural control for switched non-linear systems with multiple tracking error constraints

Here, an adaptive neural control problem for a switched non-linear system with multiple tracking error constraints is studied by using the dwell-time method. The unknown functions are approximated by radial basis function neural networks. In order to avoid the difficulty caused by the adoption of different coordinate transformations, a common transition function is selected. Moreover, different update laws are designed for both active time-interval and inactive time-interval of each subsystem. The proposed controllers and switching signals guarantee the stability of the closed-loop system and the boundedness of all signals. Furthermore, both transient-state and steady-state performances of the tracking errors are ensured. Finally, a simulation example is used to clarify the effectiveness of the proposed method.
Estimation in Step-Stress Partially Accelerated Life Tests for the Chen Distribution Using Progressive Type-II Censoring

In this paper, step-stress partially accelerated life test s (SS-PALT) are considered when the lifetime of a product fol lows a two-parameter bathtub-shaped lifetime distribution (Ch en distribution). Based on progressive Type-II censoring, the maximum likelihood estimates (MLEs) are obtained for the distribut ion parameters and acceleration factor. In addition, asymp totic variance and covariance matrix of the estimators are given. Approxim ate confidence intervals (CIs) for the parameters based on no rmal approximation to the asymptotic distribution of the MLEs an d the bootstrap (CIs) are derived. An iterative procedure is used to obtain the estimators numerically using (Mathematica Package). A numerical example is presented to illustrate the method of e stimation developed here. Finally, a Monte Carlo simulation study is p erformed to investigate the precision of the MLEs and to comp are the CIs considered.
Autonomous Flight Envelope Estimation for Loss-of-Control Prevention

A nonlinear aircraft dynamics modeling and system identification approach is formulated and used to derive a rapid, convex optimization-based algorithm for the estimation of aerodynamic force coefficients from noisy sensor data, with uncertainty quantification. Furthermore, with the aerodynamic force coefficients identified, a computationally fast method for generating the corresponding aircraft trim envelope given safe limits on achievable thrust and angle of attack is presented. This unified approach achieves the online Bayesian inference of the aircraft aerodynamic performance capability while running in the background as the aircraft is controlled independently by the pilot or automation system. The resulting information is readily incorporated into the determination of the extended safe maneuvering envelope, pilot displays, and automation algorithms for flight planning, trajectory generation, and guidance: all to help maintain safe aircraft operations under both nominal and off-nominal flight conditions.
Ultrafast emission from colloidal nanocrystals under pulsed X-ray excitation

Fast timing has emerged as a critical requirement for radiation detection in medical and high energy physics, motivating the search for scintillator materials with high light yield and fast time response. However, light emission rates from conventional scintillation mechanisms fundamentally limit the achievable time resolution, which is presently at least one order of magnitude slower than required for next-generation detectors. One solution to this challenge is to generate an intense prompt signal in response to ionizing radiation. In this paper, we present colloidal semiconductor nanocrystals (NCs) as promising prompt photon sources. We investigate two classes of NCs: two-dimensional CdSe nanoplatelets (NPLs) and spherical CdSe/CdS core/giant shell quantum dots (GS QDs). We demonstrate that the emission rates of these NCs under pulsed X-ray excitation are much faster than traditional mechanisms in bulk scintillators, i.e. 5d-4f transitions. CdSe NPLs have a sub-100 ps effective decay time of 77 ps and CdSe/CdS GS QDs exhibit a sub-ns value of 849 ps. Further, the respective CdSe NPL and CdSe/CdS GS QD X-ray excited photoluminescence have the emission characteristics of excitons (X) and multiexcitons (MX), with the MXs providing additional prospects for fast timing with substantially shorter lifetimes.
An efficient iterative algorithm for quaternionic least-squares problems over the generalized -(anti-)bi-Hermitian matrices

A class of quaternion matrices called generalized -(anti-)bi-Hermitian matrices is defined which incorporates the -(anti-)bi-Hermitian matrices mentioned by Yuan et al. [Linear Multilinear Algebra. 63;2015:1849–1863] as special cases. In the earlier referred work, Yuan et al. have derived explicit formulas for the least-squares -(anti-)bi-Hermitian solutions of the coupled matrix equations. In this paper, an efficient iterative algorithm is proposed to numerically find the generalized least-squares -(anti-)bi-Hermitian solutions of the coupled matrix equations The validity and efficiency of the presented algorithm is examined by some test experiments.
Designing parametric linear quadratic regulators for parametric LTI systems via LMIs

ABSTRACT This paper addresses the problem of determining parametric linear quadratic regulators (LQRs) for continuous-time linear-time invariant systems affected by parameters through rational functions. Three situations are considered, where the sought controller has to minimise the best cost, average cost, and worst cost, respectively, over the set of admissible parameters. It is shown that candidates for such controllers can be obtained by solving convex optimisation problems with linear matrix inequality (LMI) constraints. These candidates are guaranteed to approximate arbitrarily well the sought controllers by sufficiently increasing the size of the LMIs. In particular, the candidate that minimises the average cost approximates arbitrarily well the true LQR over the set of admissible parameters. Moreover, conditions for establishing the optimality of the found candidates are provided. Some numerical examples illustrate the proposed methodology.
Spincaloritronic Measurements: A Round Robin Comparison of the Longitudinal Spin Seebeck Effect

The rising field of spin caloritronics focuses on the interactions between spin and heat currents in a magnetic material; the observation of the spin Seebeck effect opened the route to this branch of research. This paper reports the results of a round robin test performed by five partners on a single device highlighting the reproducibility problems related to the measurements of the spin Seebeck coefficient, the quantity that describes the strength of the spin Seebeck effect. This paper stimulated the search for more reproducible measurement methods through the analysis of the systematic effects.
Numerical Simulations of Impact and Cratering with Density Independent Smoothed Particle Hydrodynamics

Impacts of meteorite are important phenomenon for the planetary geology. Since these processes cannot be experimented in laboratories, numerical hydrodynamical simulations of the impact process play important role. For these processes, particle based numerical hydrodynamical simulations have several advantages over grid-based methods, because these processes often involve large deformation of target and oblique impacts. The Smoothed Particle Hydrodynamics (SPH) is a widely used particle based numerical hydrodynamical scheme. It is first developed in astrophysical field. Recently, it was adopted to the impact cratering. However, it has been pointed out that the standard SPH formulation has difficulties in the treatment of contact discontinuity; an unphysical repulsive force acts between two different materials, such as rock and water. Thus, we have developed new particle based hydrodynamical, Density Independent SPH (DISPH), which overcomes this difficulty. We have developed a new massively parallel particle based numerical hydrodynamical simulations code by means of DISPH. We adapted Framework for Developing Particle Simulator (FDPS), which enables us to perform high-performance parallel particle simulations easily. We will show the results of impacts of the tuff to the water with both DISPH and SSPH.
An SEM Algorithm for Scale Reduction Incorporating Evaluation of Multiple Psychometric Criteria

Development and refinement of self-report measures generally involves selecting a subset of indicators from a larger set. Despite the importance of this task, methods applied to accomplish this are often idiosyncratic and ad hoc, or based on incomplete statistical criteria. We describe a structural equation modeling (SEM)-based technique, based on the standardized residual variance–covariance matrix, which subsumes multiple traditional psychometric criteria: item homogeneity, reliability, convergent, and discriminant validity. SEMs with a fixed structure, but with substituted candidate items, can be used to evaluate the relative performance of those items. Using simulated data sets, we demonstrate a simple progressive elimination algorithm, which demonstrably optimizes item choice across multiple psychometric criteria. This method is then applied to the task of short-form development of the multidimensional “4Es” (Excitement, Esteem, Escape, Excess) scale, which are understood as indicators of psychological vulnerability to gambling problems. It is concluded that the proposed SEM-based algorithm provides an automatic and efficient approach to the item-reduction stage of scale development and should be similarly useful for the development of short forms of preexisting scales. Broader use of such an algorithm would promote more transparent, consistent, and replicable scale development.
Perbandingan Metode Probabilistik Naive Bayesian Classifier Dan Jaringan Syaraf Tiruan Learning Vector Quantization Dalam Kasus Klasifikasi Penyakit Kandungan

Penyakit kandungan merupakan momok menakutkan bagi kaum wanita. Hal tersebut dikarenakan kaum wanita sangat beresiko tinggi untuk terkena penyakit kandungan. Penyakit kandungan yang dimaksud dalam penelitian ini adalah radang panggul, mioma uteri, kanker serviks, dan kanker ovarium. Penelitian ini mengajukan dua pendekatan berbeda untuk penyelesaian kasus diagnosa penyakit kandungan, yaitu pendekatan probabilistik dengan Naive Bayesian Classifier dan jaringan syaraf tiruan Learning Vector Quantization. Pada pendekatan Naive Bayesian Classifier digunakan laplacian smoothing sehingga dapat menghindari kegagalan klasifikasi, yang diharapkan dapat meningkatkan tingkat akurasi dari Naive Bayesian Classifier dalam penyelesaian kasus ini. Begitu juga pada metode Learning Vector Quantization, dilakukan pemilihan model paling tepat berdasar kepada tiga parameter yaitu learning rate, learning rate reduction, dan epoh. Pengujian dilakukan dengan menggunakan prosedur random stratified and repeated holdout pada empat skenario simulasi dengan porsi data training dan testing: 20/80, 40/60, 60/40, dan 80/20. Dari hasil uji coba dapat ditunjukkan bahwa metode Naive Bayesian Classifier dengan penambahan Laplacian Smoothing memiliki tingkat akurasi pengujian paling tinggi, yaitu secara berturut-turut 88%, 92.4%, 92.8% dan 92.4% dibandingkan dengan Naive Bayesian Classifier tanpa Laplacian Smoothing (32%; 67.8%; 79%; 89.6%) dan Learning Vector Quantization (82.4%; 88.8%; 89.4%; 95.2%). Dalam hal kecepatan pembelajaran, Learning Vector Quantization dapat menyelesaikan proses pembelajaran dengan waktu yang lebih singkat dari Naive Bayesian Classifier, hal tersebut dikarenakan pada kasus ini Learning Vector Quantization dapat memberikan performa yang baik dengan jumlah epoh yang relatif kecil. Secara umum Naive Bayesian Classifier dengan Laplacian Smoothing merupakan metode paling baik dalam menyelesaikan
FeSe-based superconductors with a superconducting transition temperature of 50 K

Due to the strong reactivity of alkaline metals and the easy formation of the impurity phase, the superconducting transition temperature (Tc) of alkaline metals intercalated FeSe is usually limited to 45 K. To avoid the formation of impurity and improve the Tc, we intercalate a more chemically inert organic ion (rather than the chemically reactive alkaline metals) into FeSe single crystal in this report. A new FeSe-based superconductor, namely (TBA)0.3FeSe, with Tc of 50 K, is synthesized by intercalating FeSe single crystal with organic ion tetrabutyl ammonium (TBA+) via an electrochemical intercalation method, which has the highest Tc among FeSe-based bulk superconductors. The structure of the organic ion intercalated product consists of the alternate stacking of monolayer FeSe and the organic molecule. The superconductivity of (TBA)0.3FeSe is confirmed by both the magnetic susceptibility and the transport measurement. It is suggested that the chemically inert organic ion should play a key role in the enhancement of Tc by avoiding the formation of impurity and disorder in FeSe plane as possible. We also suggest that the TBA+ intercalated FeSe with well defined shape and higher Tc offer a good playground for further bulk measurement investigation.
Discovering Overlapping Communities Based on Line Graph and PSO

1 Abstract From the perspective of optimizing modularity, an overlapping community discovery algorithm, LGPSO, is pro- posed based on line graph and PSO. The property that a parti- tion of a line graph corresponds to a cover of the corresponding original graph is proved. LGPSO discovers overlapping com- munities in original graph using PSO to optimize partition of line graph. The experiments on some real-world networks show that the algorithm can fast and efiectively discover the intrin- sic overlapping communities in networks without any domain
Efficient simulation of 2D electromagnetic crystal structures with the multilevel fast multipole algorithm

The multilevel fast multipole algorithm is applied for the efficient simulation of large electromagnetic crystal structures. The total computational complexity of this approach is O(PN log(N)), with N the number of unknowns andP the number of iterations required for the iterative solution to the desired accuracy. To reduce P , a simple block diagonal precondi- tioner is used. As an example a four-channel multiplexer-demultiplexer is simulated.
A time marching strategy for solving parabolic and elliptic equations with Neumann boundary conditions

Abstract In the community of computational fluid dynamics, pressure Poisson equation with Neumann boundary condition is usually encountered when solving the incompressible Navier–Stokes equations in a segregated approach such as SIMPLE, PISO, and projection methods. To deal with Neumann boundary conditions more naturally and to retain high order spatial accuracy as well, a sixth-order accurate combined compact difference scheme developed on staggered grids (NSCCD6) is adopted to solve the parabolic and elliptic equations subject to Neumann boundary conditions. The staggered grid system is usually used when solving the incompressible Navier–Stokes equations. By adopting the combined compact difference concept, there is no need to discretize Neumann boundary conditions with one-sided discretization scheme which is of lower accuracy order. The conventional Crank–Nicolson scheme is applied in this study for temporal discretization. For two-dimensional cases, D’yakonov alternating direction implicit scheme is adopted. A newly proposed time step changing strategy is adopted to improve convergence rate when solving the steady state solutions of the parabolic equation. High accuracy order of the currently proposed NSCCD6 scheme for one- and two-dimensional cases are shown in this article.
LIKELIHOOD RATIO GRADIENT ESTIMATION FOR

In this paper, we develop mathematical machinery for verifying that a broad class of general state space Markov chains reacts smoothly to certain types of perturbations in the underlying transition structure. Our main result provides conditions under which the stationary probability measure of an ergodic Harris-recurrent Markov chain is differentiable in a certain strong sense. The approach is based on likelihood ratio 'change-of-measure' arguments, and leads directly to a 'likelihood ratio gradient estimator' that can be computed numerically.
Mixture Complex Correntropy for Adaptive Filter

With the development of adaptive filtering theory and its application, the research on complex domain has attracted more attention. As a measure of local similarity, complex correntropy has been applied to adaptive filtering in the complex domain. The choice of kernel function plays a very important role in the performance of corresponding algorithms. Generally, Gaussian function is used as the kernel function since it has some attractive properties, e.g., positive definiteness and convexity. However, Gaussian function is not always the best choice. In order to further improve the performance of adaptive filtering algorithm in the complex domain, this brief employs the mixture Gaussian function as the kernel function, and proposes a new fixed point algorithm based on the maximum mixture complex correntropy criterion (MMCCC). In addition, we derive the excess mean square error of the proposed fixed point algorithm for theoretical analysis. Simulation results illustrate the superiority of the MMCCC algorithm and the correctness of the theoretical analysis in this brief.
Subarray Processing for Projection-based RFI Mitigation in Radio Astronomical Interferometers

Radio Frequency Interference (RFI) is a major problem for observations in Radio Astronomy (RA). Adaptive spatial filtering techniques such as subspace projection are promising candidates for RFI mitigation; however, for radio interferometric imaging arrays, these have primarily been used in engineering demonstration experiments rather than mainstream scientific observations. This paper considers one reason that adoption of such algorithms is limited: RFI decorrelates across the interferometric array because of long baseline lengths. This occurs when the relative RFI time delay along a baseline is large compared to the frequency channel inverse bandwidth used in the processing chain. Maximum achievable excision of the RFI is limited by covariance matrix estimation error when identifying interference subspace parameters, and decorrelation of the RFI introduces errors that corrupt the subspace estimate, rendering subspace projection ineffective over the entire array. In this work, we present an algorithm that overcomes this challenge of decorrelation by applying subspace projection via subarray processing (SP-SAP). Each subarray is designed to have a set of elements with high mutual correlation in the interferer for better estimation of subspace parameters. In an RFI simulation scenario for the proposed ngVLA interferometric imaging array with 15 kHz channel bandwidth for correlator processing, we show that compared to the former approach of applying subspace projection on the full array, SP-SAP improves mitigation of the RFI on the order of 9 dB. An example of improved image synthesis and reduced RFI artifacts for a simulated image “phantom” using the SP-SAP algorithm is presented.