Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant potential of garlic and turmeric mixture–A Traditional Indonesian formulation

Garlic (Allium sativum L.) and Turmeric (Curcuma longa L.) are known as cooking spices and as traditional drugs that can control several diseases such as diabetes and hyperlipidemia. Many studies on garlic and turmeric have been done previously in the laboratory. Antioxidant deactivates free radicals which involve in many diseases such as atherosclerosis, aging, immune suppression, cancer and diabetes. Herbal medicine usually used in combination to enhance the activity. Therefore, this study was aimed to determine the antioxidant activity of combination of garlic and turmeric. In this study, antioxidant activity of garlic bulb water extract and turmeric ethanol extract had been examined in vitro using DPPH method and activity of the extract and its combination had been examined using lipid peroxidation method in vitro and in Swiss Webster female rats ex vivo. The EC50 of in vitro DPPH scavenging of garlic and turmeric extracts were 4237.61 and 104.91 µg/mL and the lipid peroxidation scavenging of garlic, turmeric, and garlic-turmeric (1:1) extracts were 4270.1, 2001.12, and 1631.13 µg/mL, respectively. Garlic and turmeric extract of 200 mg/kg body weight (bw) each and combination of both 50-50 and 200-200 mg/kg bw scavenged lipid peroxidation significantly compared to control by 35.40, 22.71, 19.75, and 34.55 % (p<0.05). Combination of garlic and turmeric extract showed higher antioxidant activity in vitro compared to each extract, but in ex-vivo study showed similar effect.
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i. d. data samples, or by imposing an `additional' projection step to control the `gradient' bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i. d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type---in the sense that they hold under the most practical assumptions ---which is made possible by means of a novel multi-step Lyapunov analysis.
XDeep: An Interpretation Tool for Deep Neural Networks

XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the input, and generates relevant interpretations as the output with the post-hoc manner. From the functionality perspective, XDeep integrates a wide range of interpretation algorithms from the state-of-the-arts, covering different types of methodologies, and is capable of providing both local explanation and global explanation for DNN when interpreting model behaviours. With the well-documented API designed in XDeep, end-users can easily obtain the interpretations for their deep models at hand with several lines of codes, and compare the results among different algorithms. XDeep is generally compatible with Python 3, and can be installed through Python Package Index (PyPI). The source codes are available at: this https URL.
Singular points detection with semantic segmentation networks

Singular points detection is one of the most classical and important problem in the field of fingerprint recognition. However, current detection rates of singular points are still unsatisfactory, especially for low-quality fingerprints. Compared with traditional image processing-based detection methods, methods based on deep learning only need the original fingerprint image but not the fingerprint orientation field. In this paper, different from other detection methods based on deep learning, we treat singular points detection as a semantic segmentation problem and just use few data for training. Furthermore, we propose a new convolutional neural network called SinNet to extract the singular regions of interest and then use a blob detection method called SimpleBlobDetector to locate the singular points. The experiments are carried out on the test dataset from SPD2010, and the proposed method has much better performance than the other advanced methods in most aspects. Compared with the state-of-art algorithms in SPD2010, our method achieves an increase of 11% in the percentage of correctly detected fingerprints and an increase of more than 18% in the core detection rate.
Fascinatin’ Rhythm – and Pauses in Translators’ Cognitive Processes

The Monitor Model fosters a view of translating where two mind modes stand out and alternate when trying to render originals word-by-word by default: shallow, uneventful processing vs problem solving. Research may have been biased towards problem solving, often operationalized with a pause of, or above, 3 seconds. This project analyzed 16 translation log files by four informants from four originals. A baseline minimal pause of 200 ms was instrumental to calculate two individual thresholds for each log file: (a) A low one – 1.5 times the median pause within words – and (b) a high one – 3 times the median pause between words . Pauses were then characterized as short (between 200 ms and the lower threshold), mid, and long (above the higher threshold, chunking the recorded activities in the translation task into task segments ), and assumed to respond to different causes. Weak correlations between short, mid and long pauses were found, hinting at possible different cognitive processes. Inferred processes did not fall neatly into categories depending on the length of possibly associated pauses. Mid pauses occurred more often than long pauses between sentences and paragraphs, and they also more often flanked information searches and even problem-solving instances. Chains of proximal mid pauses marked cases of potential hesitations. Task segments tended to happen within 4–8 minute cycles, nested in a possible initial phase for contextualization, followed by long periods of sustained attention. We found no evidence for problem-solving thresholds, and no trace of behavior supporting the Monitor Model.
A CO molecular gas wind 340 pc away from the Seyfert 2 nucleus in ESO 420-G13 probes an elusive radio jet

A prominent jet-driven outflow of CO(2–1) molecular gas is found along the kinematic minor axis of the Seyfert 2 galaxy ESO 420-G13, at a distance of 340–600 pc from the nucleus. The wind morphology resembles the characteristic funnel shape, formed by a highly collimated filamentary emission at the base, and likely traces the jet propagation through a tenuous medium, until a bifurcation point at 440 pc. Here the jet hits a dense molecular core and shatters, dispersing the molecular gas into several clumps and filaments within the expansion cone. We also trace the jet in ionised gas within the inner ≲340 pc using the [Ne II]12.8 μm line emission, where the molecular gas follows a circular rotation pattern. The wind outflow carries a mass of ∼8 × 106 M⊙ at an average wind projected speed of ∼160 km s−1, which implies a mass outflow rate of ∼14 M⊙ yr−1. Based on the structure of the outflow and the budget of energy and momentum, we discard radiation pressure from the active nucleus, star formation, and supernovae as possible launching mechanisms. ESO 420-G13 is the second case after NGC 1377 where a previously unknown jet is revealed through its interaction with the interstellar medium, suggesting that unknown jets in feeble radio nuclei might be more common than expected. Two possible jet-cloud configurations are discussed to explain an outflow at this distance from the AGN. The outflowing gas will likely not escape, thus a delay in the star formation rather than quenching is expected from this interaction, while the feedback effect would be confined within the central few hundred parsecs of the galaxy.
Breeding of Feed Grains for Western Canada

The production of livestock in western Canada is moving from a small farm system to a highly mechanized, intensive system of production with animals having superior genetics. With this change, the feed demand by the industry has been moving from using the cheapest feed available to the best quality available. Defining that quality has been a challenge. Different classes of livestock have different feed demands. As well, adoption of production practices to maintain sustainability has meant a shift from maximization of inputs/outputs to integrated crop management and optimization of resource use. Through development of tools to enhance selection, and maintenance and development of diverse germplasm, breeding of the feed grains barley (Hordeum vulgare L.) and triticale (X Triticosecale Witt.) has been an evolving effort throughout western Canada. In this review, we present the changes and challenges of feed grain breeding with focus on the breeding program at the Field Crop Development Centre (FCDC), Lacombe, AB.
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs.
Continuum states of CO

Continuum states of the CO+ cation are studied as a function of internuclear distances in the range 1.7-3.0 a(0). R-matrix wavefunctions are constructed for 1Sigma(+), (2)Pi, (2)Delta and 4Pi total symmetries by study of collisions of electrons with a CO2+ target. Complex quantum defects are obtained from above threshold energy scattering calculations, yielding resonance energy curves and corresponding electronic couplings to the CO+ continuum. Results are presented and discussed for the high-n Feshbach resonances of the CO+ spectrum converging to excited states of CO2+. These curves are responsible for the recently observed dissociative recombination of Co2+.
Sensitivity of one-neutron knockout of halo nuclei to their nuclear structure

Halo nuclei are located far from stability and exhibit a very peculiar structure. Due to their very short lifetime, they are often studied through reactions. Breakup reactions are of particular interest since their cross sections are large for these loosely-bound nuclei. Inclusive measurements of breakup–also called knockout reactions-have even higher statistics. In this proceeding, we study which nuclear-structure information can be inferred from the parallel-momentum distribution of the core of one-neutron halo nuclei after the knockout of its halo neutron. In particular, we analyse the influence of the ground-state wavefunction, the presence of excited states within the halo-nucleus spectrum and resonances in the core-neutron continuum. Our analysis shows that such observables are sensitive to the tail of the ground-state wavefunction. The presence of excited state decreases the breakup strength, and this flux is transferred to the inelastic-scattering channel. This indicates a conservation of the flux within each partial wave. We also show that the parallel-momentum distributions are insensitive to the existence of resonances within the continuum, they can thus be ignored in practice. This independence of the continuum argues that the parallel-momentum distributions are ideal observables to extract very precisely the ANCs of halo nuclei.
Impedance-Based Whole-System Modeling for a Composite Grid via Frame-Dynamics Embedding

The paper establishes a methodology to overcome the difficulty of dynamic-preserving frame alignment in ac impedance models, and thereby enables impedance-based whole-system modeling of converter-generator composite power systems. The methodology is based on an intermediary steady frame between local and global frames, which separates the global (constant) and local (dynamic) parts of the frame alignment. The local frame dynamics can be fully embedded into local impedance models via a unified transformation law. Compared to start-of-the-art impedance-based models, the proposed method retains both frame dynamics and scalability, and is generally applicable to almost all generators and converters. The methodology is used to analyze the dynamic interaction between rotors and phase-locked loops (PLLs) in a composite grid, which yields very interesting findings on the rotor-PLL stability in a low-inertia grid.
Well-Ordered "Ripple-Shaped" Microstructures of Mn Thin Films on GaAs Substrates

This study was aimed to investigate the thickness dependent morphological changes of Mn films deposited on GaAs substrates by thermal evaporation technique. Ni films were deposited under same conditions to perform comparative study of the morphological changes with respect to the Mn films. The scanning electron microscopy and atomic force microscopy studies revealed ripple-shaped structure of Mn film with good periodicity, while Ni film only exhibited small granules deposited throughout the surface. The influence of the thickness of the Mn film in producing the ripple structure was clearly observed. In addition, the annealing time was considered as the major parameter to control the ordering of the ripple structure. X-ray diffraction pattern indicated the formation of different phases of Ga-Mn and Mn-As due to diffusion of atoms during annealing. A model for the creation of stress-driven microstructure is proposed which indicates that Mn thin films grow on GaAs substrates in three stages: in the primary stage, the growth occurs via two-dimensional nucleation process; as the thickness increases, the stress is released by the film via creation of additional surface roughness which produce ripples; and finally an island-like growth occurs because of the non-uniform distribution of stress along the surface of the film.
Discovery of clinoenstatite in garnet pyroxenites from the Dabie-Sulu ultrahigh-pressure terrane, east-central China

Abstract Transmission electron microscopy (TEM) reveals that some enstatites from garnet-pyroxenites in the Chinese Dabie-Sulu ultrahigh-pressure (UHP) terrane consist of intimate intergrowths of orthoenstatite (OREN) and clinoenstatite (CLEN), and some grains of pyroxene (MgFe)2Si2O6 exhibit inclined extinction. These garnet pyroxenites are enclosed in coesite-bearing eclogite or interlayered with harzburgite and omphacitite. They contain garnet (Prp54 to Prp73), “enstatite” (En85 to En92), magnesite, and titanomagnetite (Sulu sample) or clinohumite (Dabie sample), and recrystallized at about 4.0-6.5 ± 0.2 GPa, ~750 ± 50°C. All clinoenstatite lamellae in the host orthoenstatite have even numbers of 9 Å (100) fringes without twins, and are oriented parallel to (100) of the host. The lamellae and host phases have nearly identical (MgFe)2Si2O6 compositions. The thickest observed CLEN lamellae are ~0.4 mm. Clinoenstatites from Sulu have cell parameters: a = 9.67(7) Å, b = 8.88(6) Å, c = 5.22(3) Å, b = 107.7(2)∞, and V = 427(8) Å3, and those from Dabie: a = 9.53(6) Å, b = 8.88(6) Å, c = 5.24(3) Å, b = 107.0(2)°, and V = 424(8) Å3. The TEM electron-diffraction patterns indicate that both clinoenstatites possess P21/c structures and are the low-P polymorph of clinoenstatite. The host orthoenstatites have cell dimension: a = 18.32(12) Å, b = 8.88(6) Å, c = 5.20(3) Å for the Sulu sample, and a = 18.24 (12) Å, b = 8.88(6) Å, c = 5.18(3) Å for the Dabie sample. Clinoenstatites in garnet pyroxenites from the Dabie-Sulu UHP terrane may have formed either by inversion from OREN, or by the displacive transformation from high-P clinoenstatite during decompression
DNA: Dynamic Social Network Alignment

Social network alignment, aligning different social networks on their common users, is receiving dramatic attention from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic social Network Alignment (DNA) framework, a unified optimization approach over deep neural architectures, to unfold the fruitful dynamics to perform alignment. However, it faces tremendous challenges in both modeling and optimization: (1) To model the intra-network dynamics, we explore the local dynamics of the latent pattern in friending evolvement and the global consistency of the representation similarity with neighbors. We design a novel deep neural architecture to obtain the dual embedding capturing local dynamics and global consistency for each user. (2) To model the inter-network alignment, we exploit the underlying identity of an individual from the dual embedding in each dynamic social network. We design a unified optimization approach interplaying proposed deep neural architectures to construct a common subspace of identity embeddings. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.
Weldability and Microstructural Aspects of Shielded Metal Arc Welded HSLA-100 Steel Plates

HSLA-100 steel with 14 mm thickness in quenched and tempered condition was shielded metal arc welded (SMAW) with 2 kJ/mm heat input using basic flux coated filler rods without any pre or post welding heat treatments. The steel was found to be welded satisfactorily in this condition without developing any defect. Optical microscopy studies revealed typical cast dendritic structure in the weld metal and coarse bainite in grain-coarsened area of the heat-affected zone (HAZ). Transmission electron microscopy (TEM) study confirmed incidence of mixed structure of martensite laths and bainite in weld metal, while, it was mainly of bainite laths in HAZ with evidence of martensite–austenite (M–A) constituent and massive ferrite. The yield strength (YS), ultimate tensile strength (UTS) and Charpy V-notch (CVN) impact energy of the weld metal (YS-695 MPa, UTS-842 MPa and CVN-105 J at –50°C) and HAZ (YS-790 MPa, UTS-891 MPa and CVN-130 J at –50°C) were found satisfactory although HAZ properties were inferior to the base metal properties. The hardening of HAZ was not very significant in this steel under the present welding condition.
Reasoning Over Paths via Knowledge Base Completion

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.
X-shaped Radio Galaxies: Optical Properties, Large-scale Environment, and Relationship to Radio Structure

In pursuit of clues to the origin of “winged” or X-shaped radio galaxies (XRGs) we investigate the parent galaxies of a large sample of 106 XRGs for optical–radio axes alignment, interstellar medium, black hole mass, and large-scale environment. For 41 of the XRGs it was possible to determine the optical major axis and the primary radio axis and the strong tendency for the two axes to be fairly close is confirmed. However, several counter-examples were also found and these could challenge the widely discussed backflow diversion model for the origin of the radio wings. Comparison with a well-defined large sample of normal FR II radio galaxies has revealed that: (i) XRGs possess slightly less massive central black holes than the normal radio galaxies (average masses being logMBH ∼ 8.81 M⊙ and 9.07 M⊙, respectively); (ii) a much higher fraction of XRGs (∼80%) exhibits red mid-IR colors (W2 − W3 > 1.5), indicating a population of young stars and/or an enhanced dust mass, probably due to relatively recent galaxy merger(s). A comparison of the large-scale environment (i.e., within ∼1 Mpc) shows that both XRGs and FRII radio galaxies inhabit similarly poor galaxy clustering environments (medium richness being 8.94 and 11.87, respectively). Overall, the origin of XRGs seems difficult to reconcile with a single dominant physical mechanism and competing mechanisms seem prevalent.
Human and Automatic Detection of Generated Text

With the advent of generative models with a billion parameters or more, it is now possible to automatically generate vast amounts of human-sounding text. This raises questions into just how human-like is the machine-generated text, and how long does a text excerpt need to be for both humans and automatic discriminators to be able reliably detect that it was machine-generated. In this paper, we conduct a thorough investigation of how choices such as sampling strategy and text excerpt length can impact the performance of automatic detection methods as well as human raters. We find that the sampling strategies which result in more human-like text according to human raters create distributional differences from human-written text that make detection easy for automatic discriminators.