Miracle-dz/UniFAR-FTUs huggingface dataset:
Far Ultraviolet Spectroscopic Explorer Spectroscopy of the Transitional Magnetic Cataclysmic Variable V405 Aurigae

We present Far Ultraviolet Spectroscopic Explorer (FUSE) observations of the magnetic cataclysmic variable V405 Aurigae. Together with four other DQ Her–type binaries, V405 Aur forms a small subclass of intermediate polars that are likely to evolve into low magnetic field strength polars, similar to AM Her. Our co‐added FUSE spectrum exhibits broad O vi and C iii emission lines, in addition to a narrow O vi emission‐line component that likely forms near the white dwarf surface. The O vi λ1032/λ1038 narrow emission‐line flux ratio is near 2.0 throughout the orbit, which is indicative of its formation in an optically thin gas. Radial velocity measurements of the narrow O vi emission lines restrict any orbital modulation to a very low amplitude (KWD = 2.5 ± 0.5 km s−1), indicating, along with other single‐peaked broad disk emission lines in the UV and optical, that the binary system is at low inclination.
Scaling and transport analysis of divertor conditions on the Alcator C‐Mod tokamak

Detailed measurements and transport analysis of divertor conditions in Alcator C‐Mod [Phys. Plasmas 1, 1511 (1994)] are presented for a range of line‐averaged densities, 0.7<ne<2.2×1020 m−3. Three parallel heat transport regimes are evident in the scrape‐off layer: sheath‐limited conduction, high‐recycling divertor, and detached divertor, which can coexist in the same discharge. Local cross‐field pressure gradients are found to scale simply with a local electron temperature. This scaling is consistent with classical electron parallel conduction being balanced by anomalous cross‐field transport (χ⊥∼0.2 m2 s−1) proportional to the local pressure gradient. A 60%–80% of divertor power is radiated in attached discharges, approaching 100% in detached discharges. Detachment occurs when the heat flux to the plate is low and the plasma pressure is high (Te∼5 eV). High neutral pressures in the divertor are nearly always present (1–20 mTorr), sufficient to remove parallel momentum via ion–neutral collisions.
On structures of infinite modules

Much of the literature on the structures of modules applies to those modules which possess a finite basis. The present paper is the development of a structure theory for particular infinite modules with countable bases. Generality of results is not as much the aim of the paper as is the application to problems concerning infinite matrices. For a commutative field P, Z is assumed to be a universal P-module which has a countable P-basis. A principal ideal ring Q which contains P is considered as an operator domain of S. Then the main topic studied is under what conditions submodules of Z have proper Q-bases. In the first place,, a complete characterization is given for the proper Q-bases of any Q-submodule of S. This is represented as an infinite matrix, and is called the characteristic matrix of the submodule. The finite case is studied in the third section. The results obtained are comparable with those of Ingraham and Wolf [3](1) and Chevalley [1]. The principal theorem is that every Q-module which possesses a finite Q-basis has a proper Q-basis. The concepts of primitivity-defined somewhat as Chevalley defines itand index play an important role in determining conditions for a Q-module to have a proper Q-basis. In order to find these conditions, the non-regular elements H of : are split from S. The resulting Q-module Z/H is regular. Then necessary and sufficient conditions are found for both H and Z/H to have a proper Q-basis. If the operator domain of Z be considered as Q/(m), m not a unit of Q, then in the fifth section it is seen that Z possesses a proper Q/(m)-basis. As an application of these results, Z is taken to be the set of all vectors over P of order type X which are finitely nonzero. The total operator domain of Z is a certain ring of infinite matrices, WV. Then any element A of YW, can be transformed into a direct sum of finite matrices only if Z has a proper P [A ]-basis. The algebraic theory assumed herein can be found in almost any book on modern algebra-specific attention is called to MacDuffee [4] and Zassenhaus [5 ].
Absolute measurement of square flats with Legendre polynomial fitting method

Zernike Polynomial fitting method is an effective way to reconstruct absolute surface for three-flat test. However, the Zernike circle polynomials are not orthogonal over the circular area, hence they are not suitable for square flat. We present an absolute testing of a square flat with Legendre polynomial fitting method, which relies on calculating the coefficients of the Legendre terms by least-square fitting method. To obtain the three-dimensional surface data, one additional measurement that rotate the test flat through 90° should be introduced. The formulas are derived theoretically in detail, and validity has been proved by simulated experiment. Vertical profiles of the three surfaces are compared with the measurement results obtained by three-flat test. Good agreement validates our method.
Self-supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks

We study the problem of self-supervised and interpretable data cleaning, which automatically extracts interpretable data repair rules from dirty data. In this paper, we propose a novel framework, namely Garf, based on sequence generative adversarial networks (SeqGAN). One key information Garf tries to capture is data repair rules (for example, if the city is "Dothan", then the county should be "Houston"). Garf employs a SeqGAN consisting of a generator G and a discriminator D that trains G to learn the dependency relationships ( e.g. , given a city value "Dothan" as input, the county can be determined as "Houston"). After training, the generator G can be used to generate data repair rules, but may contain both trusted and untrusted rules, especially when learning from dirty data. To mitigate this problem, Garf further updates the learned relationships with another discriminator D' to iteratively improve the quality of both rules and data. Garf takes advantages of both logical and learning-based methods, which allow cleaning dirty data with high interpretability and have no requirements for prior knowledge and training data. Extensive experiments on real-world and synthetic datasets demonstrate the effectiveness of Garf. Garf achieves new state-of-the-art data cleaning result with high accuracy, through learning from dirty datasets without human supervision.
An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier ░ ABSTRACT - Image Restoration is a significant phase to process images for their enhancement. Underwater photographs are subject to quality issues such as blurry photos, poor contrast, uneven lighting, etc. Image processing is crucial in the processing of these degraded images. This research introduced an ensemble-based classifier based on the bagging approach to enhance UW images. The support vector machine and random forest classifiers serve as the ensemble classifier's main classifiers. Additionally, to complement the feature optimization technique, the proposed ensemble classifier leverages particle swarm optimization. The feature selection method for the classifier is improved by the feature optimization process. To validate results, underwater images are collected by the Kaggle repository. In this process, Extreme Learning (EL) and Convolution Neural Network (CNN) are compared with suggested algorithms. The simulation results indicate that the proposed algorithm outperformed the existing work.
Performance Evaluation of Low Power Hybrid Combinational Circuits using Memristor

░ ABSTRACT - Recently, extending the use of memristor technology from memory to computing has received a lot of attention. Memristor-based logic design is a new concept that aims to make computing systems more efficient. Several logic families have emerged, each with its own set of characteristics. In this paper, CMOS-based hybrid memristor-based combinational circuits are designed. Many computational devices require combinational circuits. All of the proposed designs were analysed for power, latency, and transistor count. Cadence Virtuoso is used for simulation of circuits. In this study, we used the VTEAM model to describe the simulated memristor because it is easy to understand and gives accurate results.
Forecast of Carbon Consumption of a Blast Furnace using Extreme Learning Machine and Probabilistic Reasoning

Blast furnaces are chemical metallurgical reactors for the production of pig iron and slag. The raw materials used (metallic feedstock) are sinter, granulated ore and pellets. The main fuel is metallurgical coke. Considering the existing difficulties in the field of simulation of complex processes, the application of solutions based on neural networks has gained space due to its diversity of application and increase in the reliability of responses. The Extreme Learning Machine is a way to train an artificial neural network (ANN) with only one hidden layer. The database used for numerical simulation corresponds to 3.5 years of reactor operation. Big Data contains 94875 pieces of information divided into 75 variables. The input of the ELM neural network is composed of 72 variables and the output of 3 variables. The selected output variables were coke rate, PCI rate and fuel rate. Artificial neural networks using extreme learning machines and using Big Data are able to predict fuel consumption based on the parameters of the reduction process in blast furnaces, and this can be verified by the accuracy of the model.
The 5Js in Ethiopia: Amharic Hate Speech Data Annotation Using Toloka Crowdsourcing Platform

This paper presents an Amharic hate speech annotation using the Yandex Toloka crowdsourcing platform. The dataset for this paper is collected from 5 consecutive years in Ethiopia (5Js), namely from 2018-2022, following the ‘June’ events in Ethiopia. Every June for the last five years, some events put the country in violence. Accordingly, we annotate 5,267 tweets, nearly 1k tweets every year. We explore the main challenges of crowdsourcing annotation for Amharic hate speech data collection using Toloka. We attain a Fliess kappa score of 0.34 using three independent annotators that annotate the tweets and the gold label is determined using majority voting. Using the datasets, we build different classification models using classical machine learning and deep learning approaches. The classical machine learning algorithms, LR and SVM, achieve an F1 score of 0.49, while NB archives an F1 score of 0.46. The deep learning algorithms (LSTM, BiLSTM, and CNN) achieve a similar F1 score, that is 0.44, which is the lowest performance of all models. The contextual embedding models, Am-FLAIR and Am-RoBERTa achieve F1 scores of 0.48 and 0.50 respectively. We publicly release1 the dataset, source code, and models with a permissive license.1https://github.com/uhh-lt/ethiopicmodels
A Deep Reinforcement Learning Algorithm for Smart Control of Hysteresis Phenomena in a Mode-Locked Fiber Laser

We experimentally demonstrate the application of a double deep Q-learning network algorithm (DDQN) for design of a self-starting fiber mode-locked laser. In contrast to the static optimization of a system design, the DDQN reinforcement algorithm is capable of learning the strategy of dynamic adjustment of the cavity parameters. Here, we apply the DDQN algorithm for stable soliton generation in a fiber laser cavity exploiting a nonlinear polarization evolution mechanism. The algorithm learns the hysteresis phenomena that manifest themselves as different pumping-power thresholds for mode-locked regimes for diverse trajectories of adjusting optical pumping.
A note on two boundary integral formulations for particle mobilities in Stokes flow

We show that a recent publication by Liron & Barta (1992) concerning a single-layer boundary integral equation for the tractions is mathematically equivalent to Karrila & Kim's (1989) Riesz method. In actual computational schemes, the second viewpoint is preferable since the integral operator has a spectral radius less than one and even large problems can be solved by fast iterative methods.
Efficient saliency detection based on gaussian models

This study presents an efficient saliency model mainly aiming at content-based applications such as salient object segmentation. The input colour image is first pre-segmented into a set of regions using the mean shift algorithm. A set of Gaussian models are estimated on the basis of segmented regions, and then for each pixel, a set of normalised colour likelihood measures to different Gaussian models are calculated. The colour saliency measure and spatial saliency measure of each Gaussian model are evaluated based on its colour distinctiveness and the spatial distribution, respectively. Finally, the pixel-wise colour saliency map and spatial saliency map are generated by summing the colour and spatial saliency measures of Gaussian models weighted by the normalised colour likelihood measures, and they are further combined to obtain the final saliency map. Experimental results on a dataset with 1000 images and ground truths demonstrate the better saliency detection performance of our saliency model.
Dynamics of peristrophic multiplexing in holographic polymer-dispersed liquid crystal

In this article, we derive a two-dimensional nonlocal diffusion model utilising finite difference time domain method, which describes the dynamics of how multiple holographic gratings are formed in a polymer-dispersed liquid crystal film for peristrophic multiplexing storage. This model takes into account the diffusion processes of both the free monomer and the liquid crystal molecules, and the polymerisation kinetics in the overlapped regions of multiple holographic gratings. We have experimentally found out that there exists a bounce in the diffraction efficiency for multiplexed gratings at relatively low-exposure intensities, which is undesirable for recording the equal-strength holograms. Instead, the equal-strength holograms can be stored under relatively high-exposure intensities. To predict the occurrence of bounce, the temporal evolutions of the refractive index modulation are estimated and then verified by the experimental results. The appropriate holographic exposure intensity for recording the equal-strength hologram is suggested.
Tuning the overlap and the cross-layer correlations in two-layer networks: Application to a susceptible-infectious-recovered model with awareness dissemination

We study the properties of the potential overlap between two networks A,B sharing the same set of N nodes (a two-layer network) whose respective degree distributions pA(k),pB(k) are given. Defining the overlap coefficient α as the Jaccard index, we prove that α is very close to 0 when A and B are random and independently generated. We derive an upper bound αM for the maximum overlap coefficient permitted in terms of pA(k), pB(k), and N. Then we present an algorithm based on cross rewiring of links to obtain a two-layer network with any prescribed α inside the range (0,αM). A refined version of the algorithm allows us to minimize the cross-layer correlations that unavoidably appear for values of α beyond a critical overlap αc<αM. Finally, we present a very simple example of a susceptible-infectious-recovered epidemic model with information dissemination and use the algorithms to determine the impact of the overlap on the final outbreak size predicted by the model.
The Globally Linear Embedding Algorithm

LLE is a very effective non-linear dimension reduction algorithm and widely explored in machine learning, pattern recognition, data mining and etc. Locally linear, Globally non-linear has always been regarded as the features and advantages of LLE. However, the theoretical derivation presented in this paper shows that when the size of neighborhood is larger than the dimension of the space in which the data are presented, LLE is no longer global nonlinear and almost has the same effect as PCA in dimensionality reduction. At present, a lot of literatures on LLE verify their results on Swiss Roll, Punctured Sphere, Twin Peaks, etc. These manifolds are presented in the three-dimensional Euclidean space and the size of neighborhood is always larger than three to prevent too small to be effective. But in these cases, LLE cannot play its advantage of nonlinearity.
M:N phase synchronization of LFF in an chaotic ECSL system

The chaotic behavior of on external-cavity semiconductor laser (ECSL) working in a regime of low-frequency fluctuations (LFFs) can be controlled by external electro-optical phase or intensity current modulation. It is shown by numerical simulation that, for specific values of the modulating frequency and amplitude, the phase difference between the laser power drop-out and the modulator remains constant in time leading to phase-synchronized states, steady LFFs, so called m:n phase synchronization. The degree of stabilization is determined by calculating Shannon's entropy and by the analyzing the stability of the phase locking. The synchronization regions are mapped and zones of low and high amplitude chaos are identified. The light emission can be stabilized from a regime of large amplitude chaotic oscillations corresponding to LFFs to one of low-amplitude chaotic or even periodic oscillations. The dynamics of the laser can be controlled when the period of the modulating signal is comparable with the laser itinerancy time between consecutive external-cavity modes.
Low dark current small pixel large format InGaAs 2D photodetector array development at Teledyne Judson Technologies

Teledyne Judson Technologies (TJT) has been developing technology for small pixel, large format, low dark current, and low capacitance NIR/SWIR InGaAs detector arrays, aiming to produce <10μm pixels and >2Kx2K format arrays that can be operated at or near room temperature. Furthermore, TJT is now developing technology for sub-10μm pixel arrays in response to requirements for a variety of low light level (LLL) imaging applications. In this paper, we will review test data that demonstrates lower dark current density for 10-20μm pixel arrays. We will present preliminary results on the successful fabrication of test arrays with pixels as small as 5μm. In addition, a lot of effort has been made to control and reduce the detector pixel capacitance which can become another source of detector noise. TJT is also developing 4" InGaAs wafer process and now offers four different types of InGaAs 2D arrays/FPAs that are tailored to different customer requirements for dark current, capacitance, spectral response, and bias range.
Ageostrophic Pseudovorticity and Geostrophic C-Vector Forcing—A New Look at the Q Vector in Three Dimensions

Abstract By combining the two Q-vector component equations with the third quasigeostrophic (QG) diagnostic equation (the vertical ageostrophic vorticity equation) a complete set of QG diagnostic equations is formed in a three-dimensional vector form with the ageostrophic pseudovorticity vector on the left-hand side and a newly defined geostrophic forcing vector (the C vector) on the right-hand side. The horizontal projection of the C vector is a rotated Q vector (by 90° to the right). The vertical C-vector component is proportional to the Gaussian curvature of the geopotential surface of constant pressure. Since C-vector streamlines can be viewed as ageostrophic pseudovortex lines, ageostrophic circulations can be easily inferred through three-dimensional “vorticity thinking,” which considers both the boundary effect and moist processes. The C vector is interpreted physically in terms of generation of Coriolis force curl and buoyancy curl due to the geostrophic advection alone. The basic techniques and po.
RXTE observation of recent flaring activity from the transient X‐ray pulsar 2S 1553−542

We summarize the analysis of observations made by the Rossi X-ray Timing Explorer and the Swift/Burst Alert Telescope at the time of the recent flaring activity of the transient X-ray pulsar 2S 1553−542. The binary system is characterized by the projected semimajor axis ax sin i of 168 ± 18 light-seconds, the binary orbital period Porb of 29.56 ± 0.53 d and the X-ray mass function fx(M) of 5.3 ± 1.2 M⊙. We also found Pspin= 9.2829 ± 0.0003 s with s s−1, i.e. a spin-up rate of yr−1. The spin period is found to decrease more slowly than that of most other X-ray pulsars. As the flare decays with time, the structure and X-ray counts in the pulse profile showed considerable variability. However, no significant structural variability is seen in single-peaked pulse profiles in different energy bands. Pulsed fraction decreases with the decay of the flare but its value at different energy bands showed a discontinuity. Using X-ray spectroscopy at different orbital cycles, we have detected the presence of the Fe Kα emission line in the spectra with a minimum significance of 3σ. Apart from a consistent increase in blackbody temperature, other spectral parameters are found to be stable throughout orbital cycles. From detailed spectral and timing analysis, we concluded that the X-ray flare is spectrally hard and we confirmed the nature of the compact object as a transient, accretion-powered, spin-up X-ray pulsar with a Be-type companion.
A gram scale synthesis of monodispersed silver nanoparticles capped by carboxylates and their ligand exchange

In this paper, we report a simple yet powerful synthetic method for obtaining monodispersed silver nanoparticles by direct thermal decomposition of two materials — one is silver acetate as a source of the metal core and the other is myristic acid as a capping agent. The reaction was performed at 250°C, the boiling point of myristic acid, without additional solvent. The nucleation and growth of the particles were monitored by dynamic light scattering in order to optimize the reaction time. By this simple procedure, we could obtain uniformly sized Ag nanoparticles with the average diameter of 4.8 ± 0.1 nm. Although the particles were synthesized at high temperature, the ligand exchange between myristates and alkanethiolates can be achieved at room temperature. Significant characteristics of Ag nanoparticles attributed to localized surface plasmons were investigated.
Gaseous environment in LLAGN: modes of interaction with compact star nuclear population

Abstract The Galactic center, which serves as a paradigm of low-luminosity active galactic nuclei (LLAGN), hosts the Nuclear star cluster (NSC) that contains both young and more evolved stars. So far the population of the end-products of stellar evolution has not been observationally confirmed and studied, although there are hints of its presence. We study the distribution of interaction modes of a hypothetical population of neutron stars within the sphere of influence of the Sgr A* supermassive black hole (SMBH). The comparison of our models with future observations could be used to constrain the 3D structure of the Galactic center.
Plasmon waveguides on silver nanoelements

Interest in photonic nanodevices motivates search for efficient transport of energy in plasmon waveguides. Chains of silver nanoelements guide light in channels of below-the-diffraction-limit size due to surface plasmon coupling. We calculate attenuation factors in chains with several geometries of nanoplates using the Finite Difference Time Domain (FDTD) method for visible and near infrared range of wavelengths, where the Drude model of dispersion is valid. Nanoplates considered in simulations are 1 micrometer high, 50 nm thick and 380 nm long and are embedded in a medium with refractive index reaching n = 1.5. Advantages of proposed waveguides are connected with their small size and possible tuneability by adjustment of geometrical parameters. However, the waveguides highly attenuate signals due to radiation into the far field and internal damping. For the optimum considered geometry and 595 nm wavelength, the energy transmission of 2 micrometers long chain of parallel nanoplates reaches 39%.