Miracle-dz/UniFAR-FTUs huggingface dataset:
UVES Observations of QSO 0000–2620: Argon and Phosphorus Abundances in the Dust-free Damped Lyα System at zabs = 3.3901

The UV resonance transitions of neutral argon, Ar I λ1066, and of singly ionized phosphorus, P II λ963, originated in the damped Lyα system (DLA) at zabs = 3.3901 toward QSO 0000-2620, have been detected by means of the Ultraviolet-Visual Echelle Spectrograph (UVES) spectrograph at the 8.2 m ESO KUEYEN telescope. So far, this is the first measurement of Ar I and the second of P II ever performed in damped galaxies and in high-redshift objects. This DLA is well known for having one of the lowest metal abundances and dust content and the lowest fractional abundance of molecular hydrogen H2. The measured Ar abundance is [Ar/H] = -1.91 ± 0.09, which is equal to the abundances of the other α-chain elements (O, S, and Si). The similarity of the Ar abundance with the other α-chain elements implies the absence of significant photoionization by either UV background or stellar sources along the sight line throughout the damped Lyα system. Both log(Ar/O) and log(Ar/S) ratios are found close to those measured in the extragalactic H II regions and in blue compact galaxies where O is more abundant by at least 1 order of magnitude. This strengthens the universality of the Ar/O and Ar/S ratios and lends support to the existence of a universal IMF. The abundance of the nonrefractory element phosphorus [P/H] = -2.31 ± 0.10 confirms the low amount of chemical evolution in the DLA. This is the measurement of P in the most metal-poor material and shows a subsolar [P/Fe] = -0.27 value. The measured ratios [P/Si] = -0.40 ± 0.13 and [P/S] = -0.40 ± 0.13 provide evidence for a mild odd-even effect. Finally, a stringent upper limit to the population of the 3P1 level in the ground state of O I is derived, which provides a lower limit to the physical dimensions of the zabs = 3.3901 system of L > 7 pc.
Anomalies in b→s Transitions and Dark Matter

Since 2013, the LHCb collaboration has reported on the measurement of several observables associated with b→s transitions, finding various deviations from their predicted values in the Standard Model. These include a set of deviations in branching ratios and angular observables, as well as in the observables RK and RK⁎, specially built to test the possible violation of Lepton Flavor Universality. Even though these tantalizing hints are not conclusive yet, the b→s anomalies have gained considerable attention in the flavor community. Here we review new physics models that address these anomalies and explore their possible connection to the dark matter of the Universe. After discussing some of the ideas introduced in these works and classifying the proposed models, two selected examples are presented in detail in order to illustrate the potential interplay between these two areas of current particle physics.
The galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3

We aim to investigate the galaxy environment in GAMA Galaxy Groups Catalogue (G3C) using a volume-limited galaxy sample from the Kilo Degree Survey Data Release 3. The k- Nearest Neighbour technique is adapted to take into account the probability density functions of photometric redshifts in our calculations. This algorithm was tested on simulated KiDS tiles, showing its capability of recovering the relation between galaxy colour, luminosity, and local environment. The characterization of the galaxy environment in G3C groups shows systematically steeper density contrasts for more massive groups. The red galaxy fraction gradients in these groups is evident for most of group mass bins. The density contrast of red galaxies is systematically higher at group centres when compared to blue galaxy ones. In addition, distinct group centre definitions are used to show that our results are insensitive to centre definitions. These results confirm the galaxy evolution scenario which environmental mechanisms are responsible for a slow quenching process as galaxies fall into groups and clusters, resulting in a smooth observed colour gradients in galaxy systems.
Automated identification of fraudulent financial statements by analyzing data traces

Firms are obliged by law to publish accurate financial statements. Nevertheless, cases exist where publicly issued documents hide the actual bad financial statuses of companies and this is revealed years later. Since companies publish financial figures periodically, it is interesting to examine whether monitoring those values or ratios based on them can help in early detection of fraud. In this work, a dataset was constructed including 943 firm-years of Greek companies enlisted at the Athens Stock Exchange, for the period 2005-2018. Experiments using combinations of financial ratios and various machine learning algorithms were undertaken in an effort to construct a system able to automatically detect false and misleading financial statements, that were issued legitimately by firms. Several instantiations of a machine learning workflow were tested using various classifier algorithms including AdaBoost, Random Forests, and others. Experiments showed that companies that issue false financial statements can be spotted automatically for most of the cases, years before the problem is manifested. So, the potential of early detection of seemingly healthy, but in fact, distressed companies do exist. An automated tool can be constructed that should be useful for financial analysts, investors and the capital markets authorities.
Anomaly Detection in Financial Data using Deep Learning: A Comparative Analysis

Anomaly detection in the financial sector has a critical importance for financial markets, investors, and regulatory authorities. As financial environments change, real-time detection of anomalies becomes more difficult due to the increase in data speed and volume with increasing digitization. Recently, deep learning (DL) algorithms have been used as a promising approach to solving the anomaly detection problem. In this study, DL-based anomaly detection model in the financial sector is presented using various DL algorithms including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1dCNN). In addition, hyperparameter optimization is performed with the grid search method. These methods are applied to two popular financial data, Tesla's stock market and Ethereum cryptocurrency data sets. Accordingly, a comparative analysis is conducted on these data sets with DL algorithms. Experimental results clearly show that GRU algorithm has the highest prediction score in both data sets, while 1dCNN algorithm has the lowest prediction score. In addition, anomaly values are demonstrated graphically with GRU for both data sets.
Work-in-Progress: DRAM-MaUT: DRAM Address Mapping Unveiling Tool for ARM Devices

In this paper, we propose a software based technique to reveal the DRAM geometry information which is essential in studying various memory based attacks mounted on embedded devices such as Row Hammer. We apply a reverse-engineering approach for retrieving row, column, bank bits of DRAM which may significantly increase the number of bit flips in a Row Hammer test. To the best of our knowledge, the proposed technique is the first work to disclose the entire information about the DRAM chips of any embedded architecture.
A Candidate Transit Event around Proxima Centauri

We present a single candidate transit event around Proxima Centauri, found during a blind transit search using a robotic 30\,cm telescope at Las Campanas Observatory. The event lasted 1 hour, with an estimated depth of 5\,mmag, and is inconsistent with the transit window predicted for the recently discovered planet b. We modeled the lightcurve under the assumption that the event was caused by a transiting exoplanet, and our model predicts the planet has a radius $R \sim 1 R_{\oplus}$. We encourage continued monitoring of Proxima to elucidate the origin of this event.
Reduction of viscous potential for northward interplanetary magnetic field as seen in the LFM simulation

The two primary methods responsible for solar wind magnetosphere coupling are magnetic reconnection and the viscous interaction. The viscous interaction is generated due to the antisunward dragging of plasma inside the magnetopause by the plasma flowing in the magnetosheath, creating a return flow deeper inside the magnetosphere and producing a circulation pattern. This viscous circulation pattern is mapped into the ionosphere via magnetic field lines, which results in ionospheric electric field in the nonrotating Earth's frame. We measure this interaction in terms of an electric potential, the viscous potential. In this paper, we use the results obtained from the Lyon‐Fedder‐Mobarry (LFM) simulation model during periods of purely northward interplanetary magnetic field (IMF) for different solar wind velocity and ionospheric conductivity, showing a reduction of the viscous potential with increasing magnitude of northward IMF. The viscous potential is found to settle around 5–10 kV for large +Bz values. The decrease in viscous potential was found to be associated with a weak or nonexistent sunward plasma flow in the nightside plasmasheet. Instead, the return flow to the dayside occurs at high latitudes and is associated with the reconnection topology and dynamics that occur during northward IMF periods. We also show that the magnetosphere remains closed during purely northward IMF, except for two small regions—one on each hemisphere, where the magnetic reconnection occur. We argue that the reduction of the viscous potential is due to a reduction of the velocity shear across the magnetopause and the lack of sunward convection in the equatorial tail.
Metallicity calibrations for dwarf stars and giants in the Geneva photometric system

We use the most homogeneous $Geneva$ seven-colour photometric system to derive new metallicity calibrations for early A- to K-type stars that cover both, dwarf stars and giants. The calibrations are based on several spectroscopic data sets that were merged to a common scale, and we applied them to open cluster data to obtain an additional proof of the metallicity scale and accuracy. In total, metallicities of 54 open clusters are presented. The accuracy of the calibrations for single stars is in general below 0.1 dex, but for the open cluster sample with mean values based on several stars we find a much better precision, a scatter as low as about 0.03 dex. Furthermore, we combine the new results with another comprehensive photometric data set to present a catalogue of mean metallicities for more than 3000 F and G-type dwarf stars with $\sigma \sim 0.06$ dex. The list was extended by more than 1200 hotter stars up to about 8500 K (or spectral type A3) by taking advantage of their almost reddening free characteristic in the new $Geneva$ metallicity calibrations. These two large samples are well suited as primary or secondary calibrators of other data, and we already identified about 20 spectroscopic data sets that show offsets up to about 0.4 dex.
Radiative Transfer in a Clumpy Universe. II. The Ultraviolet Extragalactic Background

We present a detailed calculation of the propagation of AGN-like ionizing radiation through the intergalactic space. We model the ionization state of absorbing clouds, and show that the universe will be more opaque above $4\,$Ryd than previously estimated. Singly ionized helium in \Lya forest clouds and Lyman-limit systems is found to be very efficient in reprocessing soft X-ray, helium-ionizing photons into ultraviolet, hydrogen-ionizing ones. We demonstrate that a significant fraction of the absorbed primary photons (emitted, e.g., by quasar sources) will be reradiated by the photoionized gas through \Lya line emission, two-photon continuum, and recombination continuum radiation. In the light of new data and recent studies, we also reassess the contribution of the QSOs observed in optical surveys to the UV extragalactic background, and find that the stochastic reprocessing of quasar Lyman continuum radiation by hydrogen and helium along the line of sight will significantly affect the amplitude, spectral shape, and fluctuations properties of the metagalactic flux. In a scenario in which QSOs are the primary source of ionizing photons in the universe, the integrated \HI \Lya emission at $z=0$ from photoionized \Lya clouds and Lyman-limit systems is found to be at a level of less than 5\% of current observational limits on the far-UV extragalactic radiation flux. We show that $J_{912}$ increases from $\approx 10^{-23} \uvunits$ at the present epoch to $\approx 5\times 10^{-22}\uvunits$ at $z=2.5$. The attenuated direct quasar emission plus recombination radiation from intergalactic gas appears to provide enough hydrogen-ionizing photons to satisfy the proximity effect at large redshift. The \HeII/\HI ratio in the diffuse intergalactic medium and the \Lya clouds increases from $\approx25$ at
Classification of Emotional States from Multi-channel EEG Signals by Convolutional Neural Networks

Assistive technologies have great importance in providing comfortable life by today. Perception of the emotions by the interfaces is also an important concern, especially for Human Machine Interaction (HMI) studies; however, the performance of the recent tools is not sufficient. Hence, these studies need to be improved. For this goal, it was aimed to provide trustable and efficient emotion classification methods to improve the previous studies' performances. In the literature, there are several feature extraction and classification algorithms used due to the non-linear characteristics of electroencephalogram (EEG) signals. In this study, one of the performance achiever models of the literature, which is Convolutional Neural Networks (CNN), selected and basic signal analysis methods were applied as preprocessing techniques. To examine the performance of the classifier, the DEAP dataset, which is one of the commonly preferred and reliable datasets, was chosen, and valance/ar ousal states were classified with CNN by using raw EEG signals, Fourier, and wavelet features. It was observed that CNN provides a successful performance even for raw signals where wavelet features transform features outperform raw and Fourier features.
MULTI-FREQUENCY STUDIES OF RADIO RELICS IN THE GALAXY CLUSTERS A4038, A1664, AND A786

We present a multi-frequency study of radio relics associated with the galaxy clusters A4038, A1664, and A786. Radio images, integrated spectra, spectral index maps, and fits to the integrated spectra in the framework of the adiabatic compression model are presented. Images of the relic in A4038 at 150, 240, and 606 MHz with the Giant Meterwave Radio Telescope have revealed extended ultra-steep spectrum (α ∼ −1.8 to −2.7) emission of extent 210 × 80 kpc2. The model of passively evolving radio lobes compressed by a shock fits the integrated spectrum best. The relic with a circular morphology at the outskirts of the cluster A1664 has an integrated spectral index of ∼ − 1.10 ± 0.06 and is best fit by the model of radio lobes lurking for ∼4 × 107 yr. The relic near A786 has a curved spectrum and is best fit by a model of radio lobes lurking for ∼3 × 107 yr. At 4.7 GHz, a compact radio source, possibly the progenitor of the A786 relic, is detected near the center of the radio relic. The A786 radio relic is thus likely a lurking radio galaxy rather than a site of cosmological shock as has been considered in earlier studies.
Masses and Radii of Four Very Low-mass Stars in F+M Eclipsing Binary Systems

Eclipsing binaries (EBs) with one of the companions as very low-mass stars (VLMSs; or M dwarfs) are testbeds to substantiate stellar models and evolutionary theories. Here we present four EB candidates with F-type primaries, namely, SAO 106989, HD 24465, EPIC 211682657, and HD 205403, identified from different photometry missions, SuperWASP, Kilodegree Extremely Little Telescope (KELT), Kepler 2 (K2), and Solar Terrestrial Relations Observatory (STEREO). Using the high-resolution spectrograph PRL Advanced Radial velocity Abu-sky Search at the 1.2 m telescope at Mount Abu, Rajasthan, India, we hereby report the detection of four VLMSs as companions to the four EBs. We performed spectroscopic analysis and found the companion masses to be 0.256 ± 0.005, 0.233 ± 0.002, 0.599 ± 0.017, and 0.406 ± 0.005 M⊙ for SAO 106989, HD 24465, EPIC 211682657, and SAO 106989B, respectively. We determined orbital periods of 4.39790 ± 0.00001, 7.19635 ± 0.00002, 3.142023 ± 0.000003, and 2.444949 ± 0.000001 days and eccentricities of 0.248 ± 0.005, 0.208 ± 0.002, 0.0097 ± 0.0008, and 0.002 ± 0.002 for EBs SAO 106989, HD 24465, EPIC 211682657, and HD 205403, respectively. The radii derived by modeling the photometry data are 0.326 ± 0.012 R⊙ for SAO 106989, 0.244 ± 0.001 R⊙ for HD 24465B, 0.566 ± 0.005 R⊙ for EPIC 211682657B, and 0.444 ± 0.014 R⊙ for HD 205403B. The radii of HD 24465B and EPIC 211682657B have been measured by precise Kepler photometry and are consistent with theory within the error bars. However, the radii of SAO 106989B and HD 205403B, measured by KELT and STEREO photometry, are 17%–20% higher than those predicted by theory. A brief comparison of the results of the current work is made with the M dwarfs already studied in the literature.
A Review of Neural Networks for Anomaly Detection

Anomaly detection is a critical issue across several academic fields and real-world applications. Artificial neural networks have been proposed to detect anomalies from different input types, but there is no clear guide to deciding which model to use in a specific case. Therefore, this study examines the most relevant Neural Network Outlier Detection algorithms in the literature, compares their benefits and drawbacks in some application scenarios, and displays their outcomes in benchmark datasets. The initial search revealed 1422 papers on projects completed between 2017 and 2021. These papers were further narrowed based on title, abstract, quality assessment, inclusion, and exclusion criteria, remaining 76 articles. Finally, we reviewed these publications and verified that Autoencoder Neural Network, Convolutional Neural Network, Recurrent Neural Network, and Generative Adversarial Network have promisor outcomes for outlier detection, the advantages of these neural networks for outlier detection, and the significant challenges of outlier detection strategies.
Resonances in the E-Polarized Terahertz Wave Scattering and Absorption by a Graphene Strip on-Substrate Grating

We consider the scattering and absorption of the E-polarized plane wave by infinite grating of flat graphene strips lying on flat dielectric substrate. To build a meshless full-wave algorithm, we apply the analytical regularization, based on the use of Discrete Inverse Fourier Transform. Then, the scattering problem is cast to a Fredholm 2nd-kind matrix equation for the unknown Floquet harmonic amplitudes. Therefore, the convergence of the resulting code is guaranteed. Numerical experiments show that such a configuration is a frequency-selective metasurface. If the grating period and substrate thickness are micrometer-sized, the resonance frequencies of such a periodic cavity are in the terahertz range. They correspond to low-Q natural modes of the substrate, and ultrahigh-Q lattice modes of the whole grating.
Dynamic Interactive Learning for Lightweight Detectors in Remote Sensing Imagery

The lightweight model has played an important role in the remote sensing (RS) realm. The existing researchers have proposed many models with lightweight structures, but their performance still has a gap compared with the deep model. A promising approach to optimize the lightweight model is knowledge distillation (KD), which can be viewed as knowledge transfer from the teacher model. However, the existing KD approaches have some issues. On one hand, offline distillation methods usually ignore the interactive learning between the student model and the teacher model. On the other hand, knowledge transfer does not consider instance property. This offline distillation strategy without property perception may not suitable for multiscale, diverse, and complex RS instances and results in a suboptimum training status. In this article, we propose a dynamic interactive learning (DIL) framework for optimizing RS lightweight detectors. First, we propose an instance interaction learning module. It calculates the value of every instance in the batch of the teacher and student prediction by each model’s real-time state and instance property. Then according to the DIL thought, we facilitate the low-quality instance to learn from the high-quality one whether it is from the teacher or student model. Moreover, we also propose the instance property perception (IPP) strategy that weighs the distillation knowledge of instances according to their feature, category, and location property. In the proposed DIL framework, both the teacher and student models are trained together and it is cost-free in the testing phase. Extensive experiments on three RS datasets demonstrate the effectiveness of the DIL.
DOA Tracking With Nested Arrays via an Improved Multisource Multi-Bernoulli Filtering in the Presence of Impulsive Noise

Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filtering algorithm based on phased fractional lower order moment is proposed. In the IMSMB algorithm, an unscented transform method is used to operate the surviving particles, which increases the diversity of resampling particles. Moreover, the proposed method is extended to nested array to improve tracking accuracy. Simulation results verify the superiority of the proposed method compared to the existing DOA tracking algorithm.
Microcanonical Thermodynamics of First Order Phase Transitions studied in the Potts Model

Phase transitions of ﬁrst and second order can easily be distinguished in small systems in the microcanonical ensemble. Conﬁgurations of phase coexistence, which are suppressed in the conventional canonical formulation, carry important information about the main characteristics of ﬁrst order phase transitions like the transition temperature, the latent heat, and the interphase surface tension. The characteristic backbending of the microcanonical caloric equation of state T ( E ) (not to be confused with the well known Van der Waals loops in ordinary thermodynamics or mean ﬁeld approximations) leading to a negative speciﬁc heat is intimately linked to the interphase surface entropy.
Molecular diagnosis of bovine tuberculosis on postmortem carcasses during routine meat inspection in Benin: GeneXpert® testing to improve diagnostic scheme

Background and Aim: Bovine tuberculosis (TB) is a zoonotic disease of major public health importance, particularly in African countries, where control measures are limited or largely not applied. This study aimed to determine the accuracy of the currently used bovine TB diagnostic method at slaughterhouses in Benin; this is to contribute to the betterment and improvement in the epidemiological surveillance of the disease in the country. Materials and Methods: A total of 40 tissue samples were collected from meat/viscera (lung, liver, heart, kidney, and the gastro-intestinal tract tissues) at Cotonou slaughterhouses from ruminants suspected to be infected with bovine TB during routine meat inspection. The collected samples were analyzed using GeneXpert testing technique as a reference method. Results: Twenty-six samples tested positive out of the 40 suspected tissue samples collected by GeneXpert diagnostic technique; this shows the limitation of the routine meat inspection in detecting bovine TB as currently performed in Benin. Conclusion: The outcome of the use of the molecular technique, therefore, supports the importance of the use of a molecular tool alongside the routine meat inspection for a better understanding of the epidemiology of bovine TB in Benin. However, more robust technical and policy efforts are needed for a sustainable implementation of such a strategy.