Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust U‐Net: Development of robust image enhancement model using modified U‐Net architecture

The image dehazing stage is used significantly as a preprocessing step for various applications such as remote sensing and long range imaging and automatic driver assistance system. Images acquired under low illumination, fog and snow conditions frequently show qualities like low contrast and low brightness, which genuinely influence the enhanced abstract visualization on natural eyes and extraordinarily limit the exhibition of different machine vision frameworks. The images that are captured in low‐light or heavy fog might have salient features that cannot be extracted using standard computer vision systems. A good way to get the enhanced image is to determine the transmission map (haze density or low illumination parameters) of air‐light media from each pixel of the input image. In this article, an improved U‐Net architecture is proposed to enhance images and provide robust performance metrics against the existing methods. In this model, the pooling operations in generalized U‐Net architecture are replaced by discrete wavelet transform based on up and down samplings. An attention module is developed by fusing both up and down samples to identify the missing information of low‐level features in up‐samples. The proposed architecture for U‐Net tested with different datasets: See‐in‐the‐Dark (SID) dataset, Exclusively Dark Image Dataset (ExDark), Realistic Single Image Dehazing (RESIDE) dataset, and few real‐time images and achieves superior performance metrics in terms of PSNR, MSE, and SSI when compared to the other state‐of‐art methods.
Symmetry Breaking in Pedestrian Dynamics

When two pedestrians travelling in opposite directions approach one another, each must decide on which side (the left or the right) they will attempt to pass. If both make the same choice then passing can be completed with ease, while if they make opposite choices an embarrassing stand-off or collision can occur. Pedestrians who encounter each other frequently can establish "social norms" that bias this decision. In this study we investigate the effect of binary decision-making by pedestrians when passing on the dynamics of pedestrian flows in order to study the emergence of a social norm in crowds with a mixture of individual biases. Such a situation may arise, for instance, when individuals from different communities mix at a large sporting event or at transport hubs. We construct a phase diagram that shows that a social norm can still emerge provided pedestrians are sufficiently attentive to the choices of others in the crowd. We show that this collective behaviour has the potential to greatly influence the dynamics of pedestrians, including the breaking of symmetry by the formation of lanes.
Comparative Effects of Dynamic Stretching and Ice Bag Application On the Physical Performance in Recreational Basketball Players: A Randomized Crossover Study

Introduction: Researchers found that cryotherapy at the ankle joint heightened adjacent muscle activity and reflex amplitude, which facilitated greater force production at the ankle complex. Furthermore, cryotherapy appears to increase musculoarticular stiffness, which has been associated with heightened muscular performance at a joint. Aim and objective: the aim of the study is to examine the influence of dynamic stretching and IBA technique on the physical performance. Methodology: Total 20 healthy male basketball athletes encompassing two groups participated in the current study. The study consisted of two groups, Group A (ice bag application) and Group B (dynamic stretching). Cold compress was applied over the anterior thigh, posterior thigh, and calf. Subjects were assigned to Group A, and Group B, using Convenience sampling method. Each candidate performed the 5 minutes of jogging before taking intervention. All subjects performed three functional performance tests: Vertical Jump Test, Agility T-test & 20-meter sprint. Each participant attended an orientation session to become familiar with the testing procedures. Subjects were randomly assigned into two groups and exposed to a crossover study design. The experiment was performed on two separate occasions whereby one group received the dynamic stretching in the first session, while the other group uses the cryotherapy first. After 48 hours, (cooling session) on the next occasion, the groups were changed and the second group receives the cryotherapy, while the first group performs dynamic stretching. On both occasions, the dynamic stretching and cryotherapy interventions were the same. Between the sessions, the subjects will not allow to participate in any kind of vigorous physical activity. Results: The present study showed an increase in performance of recreational Basketball players by the combination of 5 minutes of warm up plus 6 min of dynamic stretching on the vertical jump height and 20-meter sprint.
Integration-by-parts reductions from the viewpoint of computational algebraic geometry

Integration-by-parts reductions play a central role in perturbative QFT calculations. They allow the set of Feynman integrals contributing to a given observable to be reduced to a small set of basis integrals, and they moreover facilitate the computation of those basis integrals. We introduce an efficient new method for generating integration-by-parts reductions. This method simplifies the task by making use of generalized-unitarity cuts and turns the problem of finding the needed total derivatives into one of solving certain polynomial (so-called syzygy) equations.
METAREG: Stata module to perform meta-analysis regression

metareg performs random-effects meta-regression on study-level summary data. This is a revised version of the program originally written by Stephen Sharp (STB-42, sbe23). The major revisions involve improvements to the estimation methods and the addition of an option to use a permutation test to estimate p-values, including an adjustment for multiple testing. We have also made additions to the output, added an option to produce a graph, and included support for the predict command.
Including the pulsar-term in continuous gravitational-wave searches using pulsar timing arrays: a blessing and a curse

We describe several new techniques which accelerate Bayesian searches for continuous gravitational-wave emission from supermassive black-hole binaries using pulsar timing arrays. These techniques mitigate the problematic increase of search-dimensionality with the size of the pulsar array which arises from having to include an extra parameter per pulsar as the array is expanded. This extra parameter corresponds to searching over the phase of the gravitational-wave as it propagates past each pulsar so that we can coherently include the pulsar-term in our search strategies. Our techniques make the analysis tractable with powerful evidence-evaluation packages like MultiNest. We find good agreement of our techniques with the parameter-estimation and Bayes factor evaluation performed with full signal templates, and conclude that these techniques make excellent first-cut tools for detection and characterisation of continuous gravitational-wave signals with pulsar timing arrays. Crucially, at low to moderate signal-to-noise ratios the factor by which the analysis is sped up can be > 100, permitting rigorous programs of systematic injection and recovery of signals to establish robust detection criteria within a Bayesian formalism.
Coherent spin-networks

In this paper we discuss a proposal of coherent states for loop quantum gravity. These states are labeled by a point in the phase space of general relativity as captured by a spin-network graph. They are defined as the gauge-invariant projection of a product over links of Hall's heat kernels for the cotangent bundle of SU(2). The labels of the state are written in terms of two unit vectors, a spin and an angle for each link of the graph. The heat-kernel time is chosen to be a function of the spin. These labels are the ones used in the spin-foam setting and admit a clear geometric interpretation. Moreover, the set of labels per link can be written as an element of SL(2,C). These states coincide with Thiemann's coherent states with the area operator as complexifier. We study the properties of semiclassicality of these states and show that, for large spins, they reproduce a superposition over spins of spin-networks with nodes labeled by Livine-Speziale coherent intertwiners. Moreover, the weight associated to spins on links turns out to be given by a Gaussian times a phase as originally proposed by Rovelli.
The populations of hard X-ray and γ-ray sources: a correlation study and new possible identifications

Aims. We present the results of our analysis devoted to researching Galactic and extragalactic sources emitting in the energy bands surveyed by both the Swift-BAT and the Fermi-LAT telescopes. Methods. We cross-correlated the Fermi-LAT 1-year point source catalogue (1FGL) of gamma-ray sources and the second Palermo BAT catalogue (2PBC) of hard X-ray sources, establishing a correspondence between sources when their error boxes overlap. With dedicated software for reducing BAT data, we also extracted the value of the detection significance σ in the BAT 15–150 keV all-sky map in the direction of the 1FGL sources and took those above the threshold σ � = 3 into account. Results. We obtain 62 firm correspondences by cross-correlating the 1FGL and 2PBC catalogues and verifying the agreement between the identified counterparts. The number of objects emitting in both the hard X-ray and the γ-ray energy bands rises to 104 (∼7% of all the 1FGL objects) when considering all the sources with a detection significance down to σ � . This group of objects is made of 82 extragalactic sources, the largest number of which are blazars, 15 Galactic sources, and seven unidentified objects. We discuss the blazar content of the collected 1FGL-2PBC sources and compare their redshift distribution with that of the whole blazar population as reported in the second edition of the BZCAT blazar catalogue. We describe in greater detail the properties of two unidentified sources at low Galactic latitude, 1FGL J0137.8+5814 and 1FGL J2056.7+4938, and support their classification as blazars after the analysis of their broad-band spectral energy distribution.
Feeding versus feedback in AGN from near-infrared Integral Field Spectroscopy X: NGC 5929

We present near-infrared emission-line flux distributions , excitation and kinematics, as well as stellar kinematics, of the inner 520× 520 pc 2 of the Seyfert 2 galaxy NGC 5929. The observations were performed with the Gemini’s Near-Infrar ed Integral Field Spectrograph (NIFS) at a spatial resolution of∼ 20 pc and spectral resolution of 40 km s −1 in the J- and Kl-bands. The flux distributions of H 2, [Feii], [Pii], and H recombination lines are extended over most of the field of view, with the highest intensity leve ls observed along PA = 60/240 ◦ , and well correlated with the radio emission. The H2 and [Feii] line emission are originated in thermal processes, mainly due to heating of the gas by X-rays from the central Active Galactic Nucleus (AGN). Contribution of shocks due to the radio jet is observed at locations co-spatial with the radio hotspots at 0. ′′ 50 northeast and 0. 60 southwest of the nucleus, as evidenced by the emission-line ratio and gas kinematics. The stellar k inematics shows rotation with an amplitude at 250pc from the nucleus of∼200 km s −1 after corrected for the inferred inclination of 18.3 ◦ . The stellar velocity dispersion obtained from the integra ted K-band spectrum isσ∗ = 133± 8 km s −1 , which implying on a mass for the supermassive black hole of M• = 5.2 1.6 −1.2 × 10 7 M⊙, using the M•−σ∗ relation. The gas kinematics present three components: (1) gas in the plane of the galaxy in counter-rotation relative to the s tars; (2) an outflow perpendicular to the radio jet that seems to be due to an equatorial AGN outflo w; (3) turbulence of the gas observed in association with the radio hot spots, supporting an interaction of the radio jet with the gas of the disk. We estimated the mass of ionized and warm molecular gas of ∼ 1.3× 10 6 M⊙ and∼ 470 M⊙, respectively.
Identifying the Language Requirements of English for Tourism at Toraja Christian University of Indonesia

The purpose of this study is to identify the language requirements associated with English for Tourism. The respondent of the current research were Students in the sixth semester of the English Department at UKI Toraja who have recently completed the English for Tourism course. The researcher employed accidental sampling. Data were collected through interviews. The results of this research show that the language requirements needed in learning English for Tourism pertain to: Tourism knowledge, Accommodation, Culinary Tour [ pa’piong (meat cooked in bamboo), Torajan Culture [tradition, music, and art] tradition: rambutuka’ (wedding party) and rambu solo’ (funeral ceremony). , art: Ma’badong ( Traditional dance), pompang ( Traditional music). This suggests that the topic for the English for Tourism course should be developed through Torajan culture.
HYPERGROUPS AND GENERAL FUZZY AUTOMATA

In this paper, we first define the notion of a complete general fuzzy automaton with threshold c and construct an H - group, as well as commuta- tive hypergroups, on the set of states of a complete general fuzzy automaton with threshold c. We then define invertible general fuzzy automata, discuss the notions of "homogeneity, "separation, "thresholdness connected, "thresh- oldness inner irreducible and "principal and strongly connected, as applied to them and use these concepts to construct a quasi-order hypergroup on an invertible general fuzzy automaton. Finally, we derive relationships between the properties of an invertible general fuzzy automaton and the induced hy- pergroup.
INTERNAL KINEMATICS OF GROUPS OF GALAXIES IN THE SLOAN DIGITAL SKY SURVEY DATA RELEASE 7

We present measurements of the velocity dispersion profile (VDP) for galaxy groups in the final data release of the Sloan Digital Sky Survey (SDSS). For groups of given mass, we estimate the redshift-space cross-correlation function (CCF) with respect to a reference galaxy sample, ξ(s)(rp, π), the projected CCF, wp(rp), and the real-space CCF, ξcg(r). The VDP is then extracted from the redshift distortion in ξ(s)(rp, π), by comparing ξ(s)(rp, π) with ξcg(r). We find that the velocity dispersion (VD) within virial radius (R200) shows a roughly flat profile, with a slight increase at radii below ∼0.3R200 for high-mass systems. The average VD within the virial radius, σv, is a strongly increasing function of central galaxy mass. We apply the same methodology to N-body simulations with the concordance Λ cold dark matter cosmology but different values of the density fluctuation parameter σ8, and we compare the results to the SDSS results. We show that the σv − M* relation from the data provides stringent constraints on both σ8 and σms, the dispersion in log M* of central galaxies at fixed halo mass. Our best-fitting model suggests σ8 = 0.86 ± 0.03 and σms = 0.16 ± 0.03. The slightly higher value of σ8 compared to the WMAP7 result might be due to a smaller matter density parameter assumed in our simulations. Our VD measurements also provide a direct measure of the dark matter halo mass for central galaxies of different luminosities and masses, in good agreement with the results obtained by Mandelbaum et al. from stacking the gravitational lensing signals of the SDSS galaxies.
Advanced Persistent Threat Attack Detection using Clustering Algorithms

—Advanced Persistent Threat (APT) attack has become one of the most complex attacks. It targets sensitive information. Many cybersecurity systems have been developed to detect the APT attack from network data traffic and request. However, they still need to be improved to identify this attack effectively due to its complexity and slow move. It gets access to the organizations either from an active directory or by gaining remote access, or even by targeting the Domain Name Server (DNS). Nowadays, many machine learning (ML) techniques have been implemented to detect APT attack by using the tools in the market. However, still, there are some limitations in terms of accuracy, efficiency, and effectiveness, especially the lack of labeled data to train ML methods. This paper proposes a framework to detect APT attacks using the most applicable clustering algorithms, such as the APRIORI, K-means, and Hunt’s algorithm. To evaluate and compare the performance of the proposed framework, several experiments are conducted on a public dataset. The experimental results showed that the Support Vector Machine with Radial Basis Function (SVM-RBF) achieves the highest accuracy rate, reaching about 99.2%. This accurate result confirms the effectiveness of the developed framework for detecting attacks from network data traffic.
Identification of Retinal Disease using Anchor-Free Modified Faster Region

Infections of the retinal tissue, as well as delayed or untreated therapy, may result in visual loss. Furthermore, when a large dataset is involved, the diagnosis is prone to inaccuracies. As a consequence, a completely automated model of retinal illness diagnosis is presented to eliminate human input while maintaining high accuracy classification findings. ODALAs (Optimal Deep Assimilation Learning Algorithms) are unable to handle zero errors or covariance or linearity and normalcy. DLTs (Deep Learning Techniques) such as GANs (Generative Adversarial Networks) or CNNs might replace the numerical solution of dynamic systems (Convolution Neural Networks), in order to speed up the runs. With this objective, this study proposes a completely automated multi-class retina disorders prediction system in which pictures from the Fundus image dataset are upgraded using RSWHEs (Recursive Separated Weighted Histogram Equalizations) to boost contrast and noise is eliminated using the Wiener filter. The improved picture is used for segmentation, which is done using clustering and the optimum threshold. The suggested EFFCM is used for clustering (Enriched Fast Fuzzy C Means). The suggested AOO (Adaptive optimum Otsu) threshold technique is used for clustering and picture optimal thresholding. This work suggests AMF-RCNNs (anchor-free modified faster region-based CNNs) that integrate AFRPNs (anchor free regions proposal generation networks) with Improved Fast R-CNNs into single networks for detecting retinal issues accurately. The performances of Accuracy is 98.5%, F-Measure is 96.5%, Precession is 99.2% and different Subset features are 98.5 % show better results when compared with other related techniques or models. Keywords—Retinal disease; fundus image dataset; contrast enhancement; segmentation; Fast Fuzzy C Means; adaptive optimal OTSU; faster region-based convolutional neural network
Student’s Performance Prediction based on Personality Traits and Intelligence Quotient using Machine Learning

—Apparently, most life activities that people perform depend on their unique characteristics. Personal characteristics vary across people, so they perform tasks in different ways based on their skills. People have different mental, psychological, and behavioral features that affect most life activities. This is the same case with students at various educational levels. Students have different features that affect their academic performance. The academic score is the main indicator of the student’s performance. However, other factors such as personality features, intelligence level, and basic personal data can have a great influence on the student’s performance. This means that the academic score is not the only indicator that can be used in predicting students’ performance. Consequently, an approach based on personal data, personality features, and intelligence quotient is proposed to predict the performance of university undergraduates. Five machine learning techniques were used in the proposed approach. In order to evaluate the performance of the proposed approach, a real student’s dataset was used, and various performance measures were computed. Several experiments were performed to determine the impact of various features on the student’s performance. The proposed approach gave promising results when tested on the dataset.
Dynamic Entity Memory Network for Dialogue Relational Triplet Extraction

Relational triplet extraction (RTE) is a crucial 001 task in information extraction and has aroused 002 extensive attention. Although advanced stud-003 ies on RTE have achieved great progress, they 004 are still insufﬁcient for supporting practical ap-005 plications, such as dialogue system and infor-006 mation retrieval. In this paper, we focus on 007 relational triplet extraction in dialogue scenar-008 ios and introduce a new task named dialogue 009 relational triplet extraction (DRTE). Instead of 010 being treated as static texts like sentences or 011 documents, dialogues should be regarded as 012 dynamic ones generated with the progress of 013 conversations. Thus, it imposes three impor-014 tant challenges, including extracting triplets in 015 real-time with incomplete dialogue context, dis-016 covering cross-utterance relational triplets, and 017 perceiving the transition of dialogue topics. To 018 tackle these challenges, we propose a Dynamic 019 Entity Memory Network (DEMN). Speciﬁcally, 020 the key of our approach is an attentional con-021 text encoder and an entity memory network. 022 The attentional context encoder learns dialogue 023 semantics utterance by utterance and dynam-024 ically captures salient contexts for each utter-025 ance. The entity memory network is devised 026 to store the entities extracted from previous ut-027 terances and for cross-utterance triplets extrac-028 tion. Meanwhile, it also tracks topic transitions 029 in real-time and forgets the semantics of triv-030 ial entities. To verify the effectiveness of our 031 model, we manually build three datasets based 032 on KdConv benchmark. Extensive experimen-033 tal results demonstrate that our model achieves 034 state-of-the-art performances. 035
Decoding-Order-Based Power Allocation (DOPA) Scheme for Non-Orthogonal Multiple Access (NOMA) Visible Light Communication Systems

Non-orthogonal multiple access (NOMA) is an effective multiple access scheme that can be used to improve considerably the spectral efficiency of indoor downlink visible light communication (VLC) systems. However, NOMA suffers from inevitable multi-user interference which degrades the system performance. In this paper, a NOMA scheme is applied in a downlink VLC system and the impact of the multi-user interference on the system performance is studied. A closed-form expression for the user symbol error rate (SER) is derived and a decoding-order-based power allocation (DOPA) method is proposed to reduce the multi-user interference and find the optimal power allocation that minimizes the SER. The significance of the proposed schemes is demonstrated by simulation. The results show that the proposed DOPA method is able to reduce effectively the multi-user interference and provide more sum rate in comparison with benchmarking schemes such as the gain ratio PA (GRPA) and the normalized gain difference PA (NGDPA) methods.