Miracle-dz/UniFAR-FTUs huggingface dataset:
IN PHYSICALLY ACTIVE FEMALE UNIVERSITY STUDENTS

Cardiovascular diseases are the leading cause of death in the world, and occur as a result of many risk factors (obesity, high blood pressure, inadequate physical activity, hyperlipoproteinemia, inadequate diet and an unhealthy lifestyle). Given that in young adults most of these risk factors tend to remain at the same level or to even increase with age, especially after the second or third decade of life. This means that if young adults are in the at-risk group in terms of cardiovascular diseases, they usually stay in that group in later life. The research problem is to examine the strength and significance of possible linear correlations of certain cardiovascular risk factors in 53 physically active female university students (PE students), aged 19 to 25 years, as well as to examine possible differences between those with different lengths of sports experience (ranging from 0 to 5, 6 to 10 and 11 to 15 years). Their baseline characteristics (age, body height and mass, body mass index - BMI, resting metabolic rate - RMR, resting heart rate - RHR and length of sports experience) were determined, as well as the linear relationships of selected cardiovascular risk factors (body fat percentage - BF%, visceral fat level - Visc F, waist circumference - WC, arterial blood pressure - SBP and DBP). The data were analyzed (descriptive statistics, the Kolmogorov-Smirnov test, Peаrson’s and Spearman’s correlation coefficient, ANOVA) using SPSS 21.0. The results of the analysis indicated a lack of statistically significant differences in cardiovascular risks factors between female PE students with different lengths of sports experience, and the existence of statistically significant (p<0.001), positive, mostly strong correlations, between most of the selected cardiovascular risk factors. When it comes to blood pressure parameters, although weak positive and statistically significant (p<0.05) correlations were established between SBP and BMI (r=.273), SBP and WC (r=.308), so as between DBP and body mass (r=.284), DBP and RHR (r=.287), DBP and RMR (r=.292), as well between DBP and WC (r=.304) and DBP and SBP (r=.571, p<0.001), it is unequivocal that the reduction of body mass at the expense of adipose tissue is necessary, as well as an increase in the moderate physical activity level and regularity, in order to reduce the risk of cardiovascular diseases in female PE students now as well as in older age.
Pop III GRBs: an estimative of the event rate for future surveys

We discuss the theoretical event rate of gamma-ray bursts (GRBs) from the collapse of massive primordial stars. We construct a theoretical model to calculate the rate and detectability of these GRBs taking into account all important feedback and recent results from numerical simulations of pristine gas. We expect to observe a maximum of N $\lesssim$ 0.2 GRBs per year integrated over at z > 6 with \textit{Swift} and N $\lesssim$ 10 GRBs per year integrated over at z > 6 with EXIST.
Spatiotemporal mapping of the neural markers of prediction error processing across multisensory and unisensory modalities

Prediction errors in the brain are indexed by two event-related potentials – MMN and P300, which are elicited upon violation of regularity in the occurrence of repetitive stimuli. While MMN reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection, P300 is associated with cognitive processes such as update in working memory. Till date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. However, the relationship between these two ERPs, specifically in terms of their underlying cortical generators, in context of prediction error propagation along the hierarchical brain across multiple modalities is an open question. Our objective in this article is two-fold. First, we reconfirm previous reports regarding the generators of MMN and P300 in sensor space through source-space analysis using an accurate individual subject level co-registration of MRI and EEG data collected from healthy humans. We demonstrate that in multisensory environments, MMN and P300 markers represent “modality-specific” and “modality-independent” information processing, respectively. Advancing an earlier understanding that multisensory contexts speed up early sensory processing , our study reveals that this temporal facilitation extends to even the later components of prediction error processing , using custom-designed experiments that allow comparisons across different modality combinations. Such knowledge can be of immense value in clinical research for determining the stages of various treatments in aging, schizophrenia and depression, and their efficacy on cognitive function.
Secondary infall and the pseudo-phase-space density profiles of cold dark matter haloes

We use N-body simulations to investigate the radial dependence of the density, ρ, and velocity dispersion, σ, in cold dark matter (CDM) haloes. In particular, we explore how closely Q ≡ ρ/σ 3 , a surrogate measure of the phase-space density, follows a power law in radius. Our study extends earlier work by considering, in addition to spherically averaged profiles, local Q estimates for individual particles, Q i ; profiles based on the ellipsoidal radius dictated by the triaxial structure of the halo, Q i (r'); and by carefully removing substructures in order to focus on the profile of the smooth halo, Q s . The resulting Q s i (r') profiles follow closely a power law near the centre, but show a clear upturn from this trend near the virial radius, r 200 . The location and magnitude of the deviations are in excellent agreement with the predictions from Bertschinger's spherical secondary-infall similarity solution. In this model, Q ∝ r ― 1.875 in the inner, virialized regions, but departures from a power-law occur near r 200 because of the proximity of this radius to the location of the first shell crossing - the shock radius in the case of a collisional fluid. Particles there have not yet fully virialized, and so Q departs from the inner power-law profile. Our results imply that the power-law nature of Q profiles only applies to the inner regions and cannot be used to predict accurately the structure of CDM haloes beyond their characteristic scale radius.
The SLoWPoKES catalog of low-mass ultra–wide binaries: A cool stars resource for testing fundamental properties and for constraining binary formation theory

We present results from the Sloan Low-mass Wide Pairs of Kinematically Equivalent Stars (SLoWPoKES) catalog of ultra-wide (103–105.5 AU), low-mass (K5–M7) common proper motion binaries. We constructed a Galactic model, based on empirical stellar number density and 3D velocity distributions, to select bona fide pairs with probability of chance alignment <5 %, making SLoWPoKES an efficient sample for followup observations. Our initial catalog contains 1342 disk dwarf, subdwarf, and white dwarf-red dwarf systems and is the largest collection of low-mass, wide binaries ever assembled. The diversity – in mass, metallicity, age, and evolutionary states – of SLoWPoKES pairs makes it a valuable resource of coeval laboratories to examine and constrain the physical properties of low-mass stars. SLoWPoKES pairs show signatures of two (or more) formation modes in the distribution of the physical separation and higher-order multiplicity. Neither dynamical dissipation of primordial triples/quadruples or dynamical capture of ejected stars can explain the observed populations by itself. We use follow-up spectroscopic observations to recalibrate the metallicity-sensitive ζTiO/CaH index by assuming that both members of the binary system have the same composition. Our new formulation is a significantly better tracer of absolute metallicity, particularly for the early-type M dwarfs. The catalogs are publicly available on a custom data visualization portal. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
A LARGE NUMBER OF z > 6 GALAXIES AROUND A QSO AT z = 6.43: EVIDENCE FOR A PROTOCLUSTER?

QSOs have been thought to be important for tracing highly biased regions in the early universe, from which the present-day massive galaxies and galaxy clusters formed. While overdensities of star-forming galaxies have been found around QSOs at 2 < z < 5, the case for excess galaxy clustering around QSOs at z > 6 is less clear. Previous studies with the Hubble Space Telescope (HST) have reported the detection of small excesses of faint dropout galaxies in some QSO fields, but these surveys probed a relatively small region surrounding the QSOs. To overcome this problem, we have observed the most distant QSO at z = 6.4 using the large field of view of the Suprime-Cam (34′ × 27′). Newly installed red-sensitive fully depleted CCDs allowed us to select Lyman break galaxies (LBGs) at z ∼ 6.4 more efficiently. We found seven LBGs in the QSO field, whereas only one exists in a comparison field. The significance of this apparent excess is difficult to quantify without spectroscopic confirmation and additional control fields. The Poisson probability to find seven objects when one expects four is ∼10%, while the probability to find seven objects in one field and only one in the other is less than 0.4%, suggesting that the QSO field is significantly overdense relative to the control field. These conclusions are supported by a comparison with a cosmological smoothed particle hydrodynamics simulation which includes the higher order clustering of galaxies. We find some evidence that the LBGs are distributed in a ring-like shape centered on the QSO with a radius of ∼3 Mpc. There are no candidate LBGs within 2 Mpc from the QSO, i.e., galaxies are clustered around the QSO but appear to avoid the very center. These results suggest that the QSO is embedded in an overdense region when defined on a sufficiently large scale (i.e., larger than an HST/ACS pointing). This suggests that the QSO was indeed born in a massive halo. The central deficit of galaxies may indicate that (1) the strong UV radiation from the QSO suppressed galaxy formation in its vicinity or (2) that star formation closest to the QSO occurs mostly in an obscured mode that is missed by our UV selection.
TESTING THE NO-HAIR THEOREM WITH EVENT HORIZON TELESCOPE OBSERVATIONS OF SAGITTARIUS A*

The advent of the Event Horizon Telescope (EHT), a millimeter-wave very long baseline interferometric array, has enabled spatially resolved studies of the subhorizon-scale structure for a handful of supermassive black holes. Among these, the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), presents the largest angular cross section. Thus far, these studies have focused on measurements of the black hole spin and the validation of low-luminosity accretion models. However, a critical input in the analysis of EHT data is the structure of the black hole spacetime, and thus these observations provide the novel opportunity to test the applicability of the Kerr metric to astrophysical black holes. Here we present the first simulated images of a radiatively inefficient accretion flow (RIAF) around Sgr A* employing a quasi-Kerr metric that contains an independent quadrupole moment in addition to the mass and spin that fully characterize a black hole in general relativity. We show that these images can be significantly different from the images of an RIAF around a Kerr black hole with the same spin and demonstrate the feasibility of testing the no-hair theorem by constraining the quadrupolar deviation from the Kerr metric with existing EHT data. Equally important, we find that the disk inclination and spin orientation angles are robust to the inclusion of additional parameters, providing confidence in previous estimations assuming the Kerr metric based on EHT observations. However, at present, the limits on potential modifications of the Kerr metric remain weak.
S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning

In this review, we aim to inspire research into 001 S elf-S upervised S hared S emantic S pace ( S5 ) 002 multimodal learning problems. We equip non-003 expert researchers with a framework of in-004 formed modeling decisions via an extensive 005 literature review, an actionable modeling check-006 list, as well as a series of novel zero-shot eval-007 uation tasks. The core idea for our S5 check-008 list lies in learning contextual multimodal in-009 teractions at various granularity levels via a 010 shared Transformer encoder with a denoising 011 loss term, which is also regularized by a con-012 trastive loss term to induce a semantic align-013 ment prior on the contextual embedding space. 014 Essentially, we aim to model human concept 015 understanding and thus learn to “put a name to 016 a face”. This ultimately enables interpretable 017 zero-shot S5 generalization on a variety of 018 novel downstream tasks. In summary, this re-019 view provides sufficient background and ac-020 tionable strategies for training cutting-edge S5 021 multimodal networks. 022
Detection and Extraction of Faces and Text Lower Third Techniques for an Audiovisual Archive System using Machine Learning

—As part of the audiovisual archive digitization project, which has become a complex field that requires human and material resources, and its automation and optimization have so far represented a center of interest for researchers and media manufacturers, in particular those linked to the integration of artificial intelligence tools in the industry, an elaborate work for the development of an optical character and face recognition model, to digitize the tasks of audiovisual archivist from the manuscript method in automation, from a TV news video. In this article, an approach to develop an example of lower third in Arabic language and facial detection and recognition for news presenter that provide accurate classification results as well as the presentation of different methods and algorithms for Arabic characters. Many studies have been presented in this area, however a satisfactory classification accuracy is yet to be achieved. The comparative state-of-the-art results adopt the latest approaches to study face recognition or OCR, but this model supports both at the same time. it will present the context of realization, the method proposed to extract the texts in the video, using machine learning, about the specificity of the Arabic language, and finally the reasons that govern the decisions taken in the steps of realization. The best results from this approach in real project at the media station was 90.60%. The dataset collected via presenters images and the character dataset via the Pytesseract library.
Flood Prediction using Deep Learning Models

—Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BI-LSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models.
Non-Linear Relational Information Probing in Word Embeddings

Pre-trained word embeddings such as Skip-001 Gram and GloVe are known to contain a myr-002 iad of useful information about words. In this 003 work, we use multilayer perceptrons (MLP) 004 to probe the relational information contained 005 in these word embeddings. Previous studies 006 that use linear models on the analogy and rela-007 tion induction tasks have shown that SkipGram 008 generally outperforms GloVe, suggesting that 009 SkipGram embeddings contain more relational 010 information than GloVe embeddings. However, 011 by using non-linear probe like MLP, our results 012 instead suggest that GloVe embeddings contain 013 more relational information than SkipGram em-014 beddings, but a good amount of that is stored 015 in a non-linear form and thus previous linear 016 models failed to reveal that. Interpreting our re-017 lation probes using post-hoc analysis provides 018 us with an explanation for this difference. 1 019
Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures

—Because of the massive amount of textual information accessible today, automated extraction text summarization is one of the most extensively used ways to organize the information. The summarization mechanisms help to extract the important topics of data from a given set of documents. Extractive summarization is one method for providing a representative summary of a text by choosing the most pertinent sentences from the original text. Extractive multidocument text summarization systems' primary goal is to decrease the quantity of textual information in a document collection by concentrating on the most crucial subjects and removing irrelevant material. In the previous research, there are several methods such as term-weighting schemes and similarity metrics used for constructing an automated summary system. There are few studies that look at the performance of combining various Semantic similarity and word weighting techniques in automatic text summarization. We evaluated numerous semantic similarity metrics in extractive multi-document text summarization in this research. In the extractive multi-document text summarization discussed in this research, we looked at numerous semantic similarity metrics. ROUGE metrics have been used to evaluate the model performance in experiments using DUC datasets. Even more, the combination formed by different semantic similarity measures obtained the highest results in comparison with the other models.
Human Position and Object Motion based Spatio-Temporal Analysis for the Recognition of Human Shopping Actions

—Retailers have long sought ways to better understand their consumers' behavior in order to deliver a smooth and enjoyable shopping experience that draws more customers every day and, as a result, optimizes income. By combining various visual clues such as activities, gestures, and facial expressions, humans may fully grasp the behavior of others. However, due to inherent problems as well as extrinsic forced issues such as a shortage of publicly available information and unique environmental variables, empowering computer vision systems to provide it remains an ongoing problem (wild). In this paper, the authors focus on identifying human activity recognition in computer vision, which is the first and by far the most important cue in behavior analysis. To accomplish this, the authors present an approach by integrating human position and object motion in order to detect and classify tasks in both temporal and spatial analysis. On the MERL shopping dataset, the authors get state-of-the-art results and demonstrate the capabilities of the proposed technique.
Fully Distributed Group Signatures

A group signature scheme is a powerful primitive by which one can sign a message as a member of a well-deﬁned group without leaking one’s individual identity, but the group manager can revoke this anonymity and reveal the identity of the signer. All existing group signature schemes incorporate a centralized manager that is a single point of trust and failure. The group issuing manager is charged with issuing credentials, and can maliciously issue bogus credentials that can be used to sign but cannot be traced to any player. The group tracing manager can choose at-will to revoke the anonymity of any group member. We present the ﬁrst group signature schemes that are completely decentralized and have no single points of trust. To accomplish this, we design a ( t, n ) threshold scheme that distributes control of the issuing manager and the tracing manager. This removes the need to trust a single entity to perform the roles of adding, revoking and tracing group members, as these actions can only be done by a threshold quorum. We present two highly eﬃcient fully distributed group signature schemes in this paper. Our ﬁrst scheme is a distributed version of the BBS group signature scheme (CRYPTO 2004), and our second scheme is a distributed variant of the Camenisch-Lysyanskaya anonymous credentialing scheme (CRYPTO 2004). Our schemes inherit all its eﬃciency features of the original centralized group signature schemes; in particular the signatures are concretely very short.
ANTIMICROBI AL RESISTANCE DESIGNING A COMPREHENSIVE MACROECONOMIC MODELING STRATEGY

Antimicrobial resistance (AMR) is a dominant and growing global health threat that led to 1.27 million deaths in 2019. Given the widespread use of antimicrobials in agriculture and industrial applications in addition to healthcare and a range of factors affecting AMR, including climate variability, demographic trends, and plastic and metal pollution, an economy-wide approach is essential to assess its macroeconomic implications. This study summarizes the existing literature on the identified factors driving AMR and reviews the factors that have been considered in existing macroeconomic studies. We highlight the limitations in the available studies and suggest how those could be overcome via an economy-wide modeling approach that integrates the factors behind the evolution of AMR. We present three frameworks to con-ceptualize the economy-wide use of antimicrobials, the epidemiology of AMR, and how AMR affects the economy in a stylized economy embedded within a more extensive system. We propose how the AMR impacts could be mapped onto economic variables, discuss the significance of these shocks, and outline how AMR evolution scenarios could be designed, particularly with reference to climate change, demographic trends, and associated socioeconomic changes. We also discuss how modeling studies could be improved to increase their utility to policymakers and increase comparability across studies. We conclude with the major policy implications arising from the study which emphasize an economy-wide one-health approach to address AMR; regulation of the antimicrobial supply chain and incentivizing innovations; global cooperation to address AMR, and alleviating uncertainties for policymaking via scaling up the surveillance of AMR, encouraging research collaboration and enabling access to data on AMR and antimicrobial consumption.
Enhanced pairing in the checkerboard Hubbard ladder

We study signatures of superconductivity in a 2--leg "checkerboard" Hubbard ladder model, defined as a one--dimensional (period 2) array of square plaquettes with an intra-plaquette hopping $t$ and inter-plaquette hopping $t'$, using the density matrix renormalization group method. The highest pairing scale (characterized by the spin gap or the pair binding energy, extrapolated to the thermodynamic limit) is found for doping levels close to half filling, $U\approx 6t$ and $t'/t \approx 0.6$. Other forms of modulated hopping parameters, with periods of either 1 or 3 lattice constants, are also found to enhance pairing relative to the uniform two--leg ladder, although to a lesser degree. A calculation of the phase stiffness of the ladder reveals that in the regime with the strongest pairing, the energy scale associated with phase ordering is comparable to the pairing scale.