Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytoremediation potential of two maize varieties cultivated on metal-particulate-contaminated soil

Arbitrary cultivation of vacant land, even within the periphery of cottage industries in devel­oping countries, particularly in Nigeria, is on the increase. Two maize varieties [ART98/SW1 (protein) and BR-9928-DMR-SR-Y (non-protein)] were planted within the vicinity of a metal recycling plant in Ile-Ife, Nigeria to assess the metal removal potentials of these maize cultivars. The experiment was conducted in two locations, each per maize variety and laid out in a ran­domised complete block design. Two biochars produced from maize stover and Milicia exelsa, each at 10 t ha-1 were used as soil amendments. Metal uptake by the two maize varieties was in the order: root > shoot > grain, with protein maize having higher bioconcentration factors: Fe 86.82, Zn 1.19, Cu 4.53, Mn 2.42 and Pb 0.15, and hence, a pathway through which animals, including humans could ingest these metals. It was concluded that maize crop is a bioaccumu­lator of metals in soil, and protein maize cultivar removes more metals than non-protein maize. Keywords: Biochar; farm waste; heavy metal; maize; particulate matter; remediation
Perceptions of grasscutter (Thryonomys swinderianus) farmers on production technologies in the Agona West Municipality, Central Region - Ghana

The study sought to assess the dynamics of grasscutter (Thryonomys swinderianus) Produc­tion Technologies being used in the Agona West Municipality in the Central Region of Ghana. Farmers were selected from 20 communities with the help of staff from the Department of Ag­riculture and were interviewed using structured questionnaires. The results revealed that though the grasscutter farmers were varied in their views, the dissemination of grasscutter technologies through video in the study area was deemed to be effective. The results of the Logistic regression model (R-Square values of 0.119 and 0.158) showed that the impact of the dissemination pro­gramme on the livelihoods of farmers who had adopted the grasscutter production technologies was best predicted by age, household size and level of education of farmers. The overall mean, 3.99 showed that the livelihoods of the farmers had highly been improved. The paper makes a strong case for a more participatory approach anchored on inclusive problem identification and formulation of research solution that will further ensure that problems that are targeted by re­search emanate from farmers. This is expected to ensure a more effective extension of research technologies to farmers and other actors along the grasscutter value chain in the area. Keywords: Grasscutter; Production Technologies; Agona West Municipality; Central Region; Ghana
Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development of PC model is encouraging in the neurobiology community, its practical applications in machine learning (e.g., image classification) have not been fully explored yet. In this paper, a novel image processing model called fast inference PC (FIPC) is presented for image representation and classification. Compared with the basic PC model, a regression procedure and a classification layer have been added to the proposed FIPC model. The regression procedure is used to learn regression mappings that achieve fast inference at test time, while the classification layer can instruct the model to extract more discriminative features. In addition, effective learning and fine-tuning algorithms are developed for the proposed model. Experimental results obtained on four image benchmark data sets show that our model is able to directly and fast infer representations and, simultaneously, produce lower error rates on image classification tasks.
Spin-Singlet and Spin-Triplet Josephson Junctions for Cryogenic Memory

Due to the ever-increasing power and cooling requirements of large-scale computing and data facilities, there is a need for low-power alternatives to CMOS. One approach under consideration is superconducting computing based on single-flux-quantum logic. Unfortunately, there is not yet a low-power, high-density superconducting memory technology that is fully compatible with superconducting logic. We are working toward developing cryogenic memory based on Josephson junctions that contain two or more ferromagnetic (F) layers. Such junctions have been demonstrated to be programmable by changing the relative direction of the F layer magnetizations. There are at least two different types of such junctions: those that carry the innate spin-singlet supercurrent associated with the conventional superconducting electrodes, and those that convert spin-singlet to spin-triplet supercurrent in the middle of the device. In this letter, we compare the performance and requirements of the two kinds of junctions. Whereas the spin-singlet junctions need only two ferromagnetic layers to function, the spin-triplet junctions require at least three. In the devices demonstrated to date, the spin-singlet junctions have considerably larger critical current densities than the spin-triplet devices. On the other hand, the spin-triplet devices have less stringent constraints on the thicknesses of the F layers, which might be beneficial in large-scale manufacturing.
Escaping Saddle Points for Zeroth-order Non-convex Optimization using Estimated Gradient Descent

Gradient descent and its variants are widely used in machine learning. However, oracle access of gradient may not be available in many applications, limiting the direct use of gradient descent. This paper proposes a method of estimating gradient to perform gradient descent, that converges to a second-order stationary point for general non-convex optimization problems. Beyond the first-order stationary properties, the second-order stationary properties are important in machine learning applications to achieve better performance. We show that the proposed model-free non-convex optimization algorithm returns an ε-second-order stationary point with $\tilde O\left( {\frac{{{d^{2 + \frac{\theta }{2}}}}}{{{\varepsilon ^{8 + \theta }}}}} \right)$ queries of the function for any arbitrary θ > 0.
Inferring the velocity of early massive stars from the abundances of extremely metal-poor stars

Context. The nature of the early generation of massive stars can be inferred by investigating the origin of the extremely metal-poor (EMP) stars, likely formed from the ejecta of one or a few previous massive stars. Aims. We investigate the rotational properties of early massive stars by comparing the abundance patterns of EMP stars with massive stellar models including rotation. Methods. Low metallicity 20 M⊙ massive stellar models with eight initial rotation rates between 0 and 70% of the critical velocity are computed. Explosions with strong fallback are assumed. The ejected material is considered to fit individually the abundance patterns of 272 EMP stars with −4 < [Fe/H] < −3. Results. With increasing initial rotation, the [C/H], [N/H], [O/H], [Na/H], [Mg/H], and [Al/H] ratios in the massive star ejecta are gradually increased (up to ∼4 dex) while the 12C/13C ratio is decreased. Among the 272 EMP stars considered, ∼40 − 50% are consistent with our models. About 60 − 70% of the carbon-enhanced EMP star sample can be reproduced against ∼20 − 30% for the carbon-normal EMP star sample. The abundance patterns of carbon-enhanced EMP stars are preferentially reproduced with a material coming from mid to fast rotating massive stars. The overall velocity distribution derived from the best massive star models increases from no rotation to fast rotation. The maximum is reached for massive stars having initial equatorial velocities of ∼550 − 640 km s−1. Conclusions. Although subject to significant uncertainties, these results suggest that the rotational mixing operating in between the H-burning shell and the He-burning core of early massive stars played an important role in the early chemical enrichment of the Universe. The comparison of the velocity distribution derived from the best massive star models with velocity distributions of nearby OB stars suggests that a greater number of massive fast rotators were present in the early Universe. This may have important consequences for reionization, the first supernovae, or integrated light from high redshift galaxies.
Classifying superconductivity in ThH-ThD superhydrides/superdeuterides

Satterthwaite and Toepke (1970 Phys. Rev. Lett. 25 741) discovered that Th4H15-Th4D15 superhydrides are superconducting but exhibit no isotope effect. As the isotope effect is a fundamental prediction of electron-phonon mediated superconductivity described by Bardeen, Cooper, and Schrieffer (BCS) its absence alludes to some other mechanism. Soon after this work, Stritzker and Buckel (1972 Zeitschrift für Physik A Hadrons and nuclei 257 1-8) reported that superconductors in the PdHx-PdDx system exhibit the reverse isotope effect. Yussouff et al (1995 Solid State Communications 94 549) extended this finding in PdHx-PdDx-PdTx systems. Renewed interest in hydrogen- and deuterium-rich superconductors is driven by the discovery of near-room-temperature superconductivity in highly-compressed H3S (Drozdov et al 2015 Nature 525 73) and LaH10 (Somayazulu et al 2019 Phys. Rev. Lett. 122 027001). Here we attempt to reaffirm or disprove our primary idea that the mechanism for near-room-temperature superconductivity in hydrogen-rich superconductors is not BCS electron-phonon mediated. To that end, we analyse the upper critical field data, Bc2(T), in Th4H15-Th4D15 (Satterthwaite and Toepke 1970 Phys. Rev. Lett. 25 741) as well as two recently discovered high-pressure hydrogen-rich phases of ThH9 and ThH10 (Semenok et al 2019 Materials Today, DOI: 10.1016/j.mattod.2019.10.005). We conclude that all known thorium super-hydrides/deuterides, to date, are unconventional superconductors—along with the heavy fermions, fullerenes, pnictides, cuprates—where we find they have Tc/TF ratios within a range of 0.008 < Tc/TF < 0.120, where Tc is the superconducting transition temperature and TF is the Fermi temperature.
Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.
Experimental probes of Stark many-body localization

Recent work has focused on exploring many-body localization (MBL) in systems without quenched disorder: one such proposal is Stark MBL in which small perturbations to a strong linear potential yield localization. However, as with conventional MBL, it is challenging to experimentally distinguish between non-interacting localization and true MBL. In this paper we show that several existing experimental probes, designed specifically to differentiate between these scenarios, work similarly in the Stark MBL setting. In particular we show that a modified spin-echo response (DEER) shows clear signs of a power-law decay for Stark MBL while quickly saturating for disorder-free Wannier-Stark localization. Further, we observe the characteristic logarithmic-in-time spreading of quantum mutual information in the Stark MBL regime, and an absence of spreading in a non-interacting Stark-localized system. We also show that there are no significant differences in several existing MBL measures for a system consisting of softcore bosons with repulsive on-site interactions. Lastly we show why curvature or small disorder are needed for an accurate reproduction of MBL phenomenology, and how this may be illustrated in experiment. This also connects with recent progress on Hilbert space fragmentation in ``fractonic'' models with conserved dipole moment.
Element Abundances of Solar Energetic Particles and the Photosphere, the Corona, and the Solar Wind

From a turbulent history, the study of the abundances of elements in solar energetic particles (SEPs) has grown into an extensive field that probes the solar corona and physical processes of SEP acceleration and transport. Underlying SEPs are the abundances of the solar corona, which differ from photospheric abundances as a function of the first ionization potentials (FIPs) of the elements. The FIP-dependence of SEPs also differs from that of the solar wind; each has a different magnetic environment, where low-FIP ions and high-FIP neutral atoms rise toward the corona. Two major sources generate SEPs: The small “impulsive” SEP events are associated with magnetic reconnection in solar jets that produce 1000-fold enhancements from H to Pb as a function of mass-to-charge ratio A/Q, and also 1000-fold enhancements in 3He/4He that are produced by resonant wave-particle interactions. In large “gradual” events, SEPs are accelerated at shock waves that are driven out from the Sun by wide, fast coronal mass ejections (CMEs). A/Q dependence of ion transport allows us to estimate Q and hence the source plasma temperature T. Weaker shock waves favor the reacceleration of suprathermal ions accumulated from earlier impulsive SEP events, along with protons from the ambient plasma. In strong shocks, the ambient plasma dominates. Ions from impulsive sources have T ≈ 3 MK; those from ambient coronal plasma have T = 1 – 2 MK. These FIP- and A/Q-dependences explore complex new interactions in the corona and in SEP sources.
The Tutte polynomial and toric Nakajima quiver varieties

For a quiver $Q$ with underlying graph $\Gamma$, we take $ {\mathcal {M}}$ an associated toric Nakajima quiver variety. In this article, we give a direct relation between a specialization of the Tutte polynomial of $\Gamma$, the Kac polynomial of $Q$ and the Poincaré polynomial of $ {\mathcal {M}}$. We do this by giving a cell decomposition of $ {\mathcal {M}}$ indexed by spanning trees of $\Gamma$ and ‘geometrizing’ the deletion and contraction operators on graphs. These relations have been previously established in Hausel–Sturmfels [6] and Crawley-Boevey–Van den Bergh [3], however the methods here are more hands-on.
Jets in the soft state in Cyg X-3 caused by advection of the donor magnetic field and unification with low-mass X-ray binaries

The high-mass accreting binary Cyg X-3 is distinctly different from low-mass X-ray binaries (LMXBs) in having powerful radio and γ-ray emitting jets in its soft spectral state. However, the transition from the hard state to the soft one is first associated with quenching of the hard-state radio emission, as in LMXBs. The powerful soft-state jets in Cyg X-3 form, on average, ∼50 d later. We interpret the initial jet quenching as due to the hard-state vertical magnetic field quickly diffusing away in the thin disc extending to the innermost stable circular orbit in the soft state, or, if that field is produced in situ, also cessation of its generation. The subsequent formation of the powerful jets occurs due to advection of the magnetic field from the donor. We find this happens only above certain threshold accretion rate associated with appearance of magnetically driven outflows. The ∼50 d lag is of the order of the viscous time-scale in the outer disc, while the field advection is much faster. This process does not happen in LMXBs due to the magnetic fluxes available from their donors being lower than that for the wind accretion from the Wolf–Rayet donor of Cyg X-3. In our model, the vertical magnetic field in the hard state, required to form the jets both in Cyg X-3 and LMXBs, is formed in situ rather than advected from the donor. Our results provide a unified scenario of the soft and hard states in both Cyg X-3 and LMXBs.
Improved Boltzmann machines with error corrected quantum annealing

Richard Y. Li,1, 2 Tameem Albash,3, 4, 5 and Daniel A. Lidar1, 2, 6, 7 Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, California 90089, USA Department of Electrical & Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico 87131, USA Center for Quantum Information and Control, University of New Mexico, Albuquerque, New Mexico 87131, USA Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA (Dated: October 4, 2019)
Helical structures in layered magnetic superconductors due to indirect exchange interactions mediated by interlayer tunneling

Motivated by the recent discovery of helical magnetic structure in RbEuFe$_{4}$As$_{4}$, we investigate interlayer ordering of magnetic moments in materials composed of spatially-separated superconducting and ferromagnetically-aligned layers. We consider the interplay between the normal and superconducting indirect exchange interaction mediated by tunneling between the conducting layers. We elaborate a recipe to evaluate the normal interlayer interaction via two-dimensional density of states of an isolated layer and demonstrate that for bands with small fillings, such interaction is typically ferromagnetic and short-range. The nearest-layer interaction is proportional to the ratio of the interlayer hopping and in-plane band width squared. On the other hand, the superconducting contribution always gives antiferromagnetic interaction and may extend over several layers when the interlayer hopping energy exceeds the superconducting gap. The frustration caused by the interplay between the normal and superconducting parts may lead to spiral ground-state magnetic configuration. The four-fold in-plane anisotropy may lock the rotation angle between the moments in the neighboring layers to $90^\circ$, as it was observed in RbEuFe$_{4}$As$_{4}$
Stochastic gradient descent for hybrid quantum-classical optimization

Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences of the prior observation that estimation of these quantities on quantum hardware results in a form of stochastic gradient descent optimization. We formalize this notion, which allows us to show that in many relevant cases, including VQE, QAOA and certain quantum classifiers, estimating expectation values with k measurement outcomes results in optimization algorithms whose convergence properties can be rigorously well understood, for any value of k. In fact, even using single measurement outcomes for the estimation of expectation values is sufficient. Moreover, in many settings the required gradients can be expressed as linear combinations of expectation values -- originating, e.g., from a sum over local terms of a Hamiltonian, a parameter shift rule, or a sum over data-set instances -- and we show that in these cases k-shot expectation value estimation can be combined with sampling over terms of the linear combination, to obtain ``doubly stochastic'' gradient descent optimizers. For all algorithms we prove convergence guarantees, providing a framework for the derivation of rigorous optimization results in the context of near-term quantum devices. Additionally, we explore numerically these methods on benchmark VQE, QAOA and quantum-enhanced machine learning tasks and show that treating the stochastic settings as hyper-parameters allows for state-of-the-art results with significantly fewer circuit executions and measurements.
Neural Network Applications in Earthquake Prediction (1994–2019): Meta-Analytic and Statistical Insights on Their Limitations

In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994–2019 period) and found two emerging trends: an increasing interest in this domain over time and a complexification of ANN models toward deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Because of the structured, tabulated nature of earthquake catalogs, and the limited number of features so far considered, simpler and more transparent machine-learning models than ANNs seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of statistical seismology (e.g., the Gutenberg–Richter law), which are already known to have minimal abilities to predict large earthquakes.
Iterative Framework of Machine-Learning Based Turbulence Modeling for Reynolds-Averaged Navier-Stokes Simulations

A framework of machine-learning (ML) based turbulence modeling for Reynolds-averaged Navier-Stokes (RANS) equations is developed to close the Reynolds stress term in the RANS equations. Several principles for turbulence modeling using ML techniques are discussed, including the effectiveness of the closure term and sufficiency of input variables for an ML model. An iterative computational process is implemented between the ML model and the RANS solver to ensure the convergence of simulations to the training data, and the transport equations of a conventional turbulence model are also solved to assist the ML modeling. A cross-case training strategy is adopted with the data from direct numerical simulations of turbulent channel flows at different Reynolds numbers, and the solver is able to predict both the mean flow field and turbulent variables correctly within the training range. A further test in a flow over periodic hills also shows an improved result, indicating a promising prediction capability of the developed model even if the model is trained only with the data of channel flows.