Miracle-dz/UniFAR-FTUs huggingface dataset:
Registration of Four Near‐Isogenic Soybean Lines of G00‐3213 for Resistance to Asian Soybean Rust

Soybean rust (SBR), caused by an obligate biotrophic basidiomycete fungus, Phakopsora pachyrhizi, arrived in the continental United States in 2004, where it has since proven to be detrimental to southern soybean [Glycine max (L.) Merr. ] production due to yield losses, environmental concerns, and expenses caused by reliance on fungicides for control. Resistance to SBR has been developed primarily by introgressing single, dominant resistance gene(s) into an elite soybean cultivar. Here we describe four near-isogenic lines (NILs) of G00-3213: G00-3213Rpp1 (Reg. No. GP-400, PI 676017), G00-3213Rpp2 (Reg. No. GP-401, PI 676018), G003213Rpp3 (Reg. No. GP-402, PI 676019), and G00-3213Rpp4 (Reg. No. GP-403, PI 676020). These NILs were developed by backcrossing rust resistance genes Rpp1, Rpp2, Rpp3, or Rpp4 into G00-3213, an elite, maturity group VII soybean line. The NILs have tawny pubescence, tan pod walls, and white flowers and had the same general appearance to the recurrent parent G00-3213 in the field in 2014. Additionally, each NIL exhibits a similar level of resistance to the GA12 P. pachyrhizi bulk isolate as the original resistant sources of the Rpp genes. These NILs will be useful as parents for public and private plant breeders, as well as for extension agents, crop consultants, and plant pathologists in conducting in-field determinations for Rpp gene effectiveness in the southern United States.
On *-Convergence of Schur–Hadamard Products of Independent Nonsymmetric Random Matrices

Let $\{x_{\alpha }\}_{\alpha \in {\mathbb {Z}}}$ and $\{y_{\alpha }\}_{\alpha \in {\mathbb {Z}}}$ be two independent collections of zero mean, unit variance random variables with uniformly bounded moments of all orders. Consider a nonsymmetric Toeplitz matrix $X_n = ((x_{i - j}))_{1 \le i, j \le n}$ and a Hankel matrix $Y_n = ((y_{i + j}))_{1 \le i, j \le n}$, and let $M_n = X_n \odot Y_n$ be their elementwise/Schur–Hadamard product. In this article, we show that almost surely, $n^{-1/2}M_n$, as an element of the *-probability space $(\mathcal {M}_n({\mathbb {C}}), \frac {1}{n}\text {tr})$, converges in *-distribution to a circular variable. With i.i. d. Rademacher entries, this construction gives a matrix model for circular variables with only $O(n)$ bits of randomness. We also consider a dependent setup where $\{x_{\alpha }\}$ and $\{y_{\beta }\}$ are independent strongly multiplicative systems (à la Gaposhkin [7]) satisfying an additional admissibility condition, and have uniformly bounded moments of all orders—a nontrivial example of such a system being $\{\sqrt {2}\sin (2^n \pi U)\}_{n \in {\mathbb {Z}}_+}$, where $U \sim \textrm {Uniform}(0, 1)$. In this case, we show in-expectation and in-probability convergence of the *-moments of $n^{-1/2}M_n$ to those of a circular variable. Finally, we generalise our results to Schur–Hadamard products of structured random matrices of the form $X_n = ((x_{L_X(i, j)}))_{1 \le i, j \le n}$ and $Y_n = ((y_{L_Y(i, j)}))_{1 \le i, j \le n}$, under certain assumptions on the link-functions $L_X$ and $L_Y$, most notably the injectivity of the map $(i, j) \mapsto (L_X(i, j), L_Y(i, j))$. Based on numerical evidence, we conjecture that the circular law $\mu _{\textrm {circ}}$, that is, the uniform measure on the unit disk of ${\mathbb {C}}$, which is also the Brown measure of a circular variable, is in fact the limiting spectral measure (LSM)of $n^{-1/2}M_n$. If true, this would furnish an interesting example where a random matrix with only $O(n)$ bits of randomness has the circular law as its LSM.
A statistical theory of cold posteriors in deep neural networks

To get Bayesian neural networks to perform comparably to standard neural networks it is usually necessary to artificially reduce uncertainty using a "tempered" or "cold" posterior. This is extremely concerning: if the prior is accurate, Bayes inference/decision theory is optimal, and any artificial changes to the posterior should harm performance. While this suggests that the prior may be at fault, here we argue that in fact, BNNs for image classification use the wrong likelihood. In particular, standard image benchmark datasets such as CIFAR-10 are carefully curated. We develop a generative model describing curation which gives a principled Bayesian account of cold posteriors, because the likelihood under this new generative model closely matches the tempered likelihoods used in past work.
Reparametrization Invariance in non-parametric Causal Discovery

Causal discovery estimates the underlying physical process that generates the observed data: does X cause Y or does Y cause X? Current methodologies use structural conditions to turn the causal query into a statistical query, when only observational data is available. But what if these statistical queries are sensitive to causal invariants? This study investigates one such invariant: the causal relationship between X and Y is invariant to the marginal distributions of X and Y. We propose an algorithm that uses a non-parametric estimator that is robust to changes in the marginal distributions. This way we may marginalize the marginals, and inspect what relationship is intrinsically there. The resulting causal estimator is competitive with current methodologies and has high emphasis on the uncertainty in the causal query; an aspect just as important as the query itself.
Effects of environment on stellar metallicity profiles of late-type galaxies in the CALIFA survey

Aims. We explore the effects of environment in the evolution of late-type galaxies by studying the radial profiles of light- and mass-weighted metallicities of galaxies in two discrete environments: field and groups. Methods. We used a sample of 167 late-type galaxies with stellar masses of 9 ≤ log(M⋆/M⊙) ≤ 12 drawn from the Calar Alto Legacy Integral Field Area (CALIFA) survey. Firstly, we obtained light- and mass-weighted stellar metallicity profiles and stellar mass density profiles of these galaxies using publicly available data. We then classified them according to their environment into field and group galaxies. Finally, we studied the metallicity of galaxies in these two environments, including a comparison of the metallicity as a function of radius, at a characteristic scale, and as a function of stellar mass surface density. As metallicity depends on galaxy mass, we took special care throughout the study to compare, in all cases, subsamples of galaxies in groups and in the field that have similar masses. Results. We find significant differences between group and field late-type galaxies in terms of their metallicity: group galaxies are systematically higher in metallicity than their field counterparts. We find that field galaxies, in general, have metallicity profiles that show a negative gradient in their inner regions and a shallower profile at larger radii. This is in contrast to the metallicity profiles of galaxies in groups, which tend to be flat in the inner regions and to have a negative gradient in the outer parts. Regarding the metallicity at the characteristic radius of the luminosity profiles, we consistently find that it is higher for group galaxies irrespective of galaxy mass. At fixed local stellar surface mass density, group galaxies are again higher in metallicity, also the dependence of metallicity on surface density is less important for group galaxies. Conclusions. The evidence of a clear difference in metallicity between group and field galaxies as a function of mass, spatial scale, and local stellar mass density is indicative of the different evolutionary paths followed by galaxies in groups and in the field. We discuss some possible implications of the observed differences.
Wide range temperature-dependent (80–630 K) study of Hall effect and the Seebeck coefficient of β-Ga2O3 single crystals

Investigation of Seebeck coefficient in ultra-wide bandgap materials presents a challenge in measurement, nevertheless, it is essential for understanding fundamental transport mechanisms involved in electrical and thermal conduction. \b{eta}-Ga2O3 is a strategic material for high power optoelectronic applications. Present work reports Seebeck coefficient measurement for single crystal Sn doped \b{eta}-Ga2O3 in a wide temperature range (80-630 K). The non-monotonic trend with large magnitude and negative sign in the entire temperature range shows electrons are dominant carriers. The structural and Raman characterization confirms the single-phase and presence of low, mid, and high-frequency phonon modes, respectively. Temperature dependent (90-350 K) Hall effect measurement was carried out as supplementary study. Hall mobility showed T1.12 for T less than 135 K and T-0.70 for T more than 220 K. Activation energies from Seebeck coefficient and conductivity analysis revealed presence of inter band conduction due to impurity defects. The room temperature Seebeck coefficient, power factor and thermal conductivity were found as 68.57 microV/K, 0.15 microW/K2cm and 14.2 W/mK, respectively. The value of the figure-of-merit for \b{eta}-Ga2O3 was found to be aprox. 0.01 (300 K).
Conservative Stochastic Optimization With Expectation Constraints

This paper considers stochastic convex optimization problems where the objective and constraint functions involve expectations with respect to the data indices or environmental variables, in addition to deterministic convex constraints on the domain of the variables. Since the underlying data distribution is unknown a priori, a closed-form solution is generally not available, and classical deterministic optimization paradigms are not applicable. State-of-the-art approaches, such as those using the saddle point framework, are able to ensure that the optimality gap as well as the constraint violation decay as <inline-formula><tex-math notation="LaTeX">$\mathcal{O}(T^{-\frac{1}{2}})$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the number of stochastic gradients. In this work, we propose a novel conservative stochastic optimization algorithm (CSOA) that achieves zero average constraint violation and <inline-formula><tex-math notation="LaTeX">$\mathcal{O}(T^{-\frac{1}{2}})$</tex-math></inline-formula> optimality gap. </p> <p>Further, we also consider the scenario where carrying out a projection step onto the convex domain constraints at every iteration is not viable. Traditionally, the projection operation can be avoided by considering the conditional gradient or Frank-Wolfe (FW) variant of the algorithm. The state-of-the-art stochastic FW variants achieve an optimality gap of <inline-formula><tex-math notation="LaTeX">$\mathcal{O}(T^{-\frac{1}{3}})$</tex-math></inline-formula> after <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> iterations, though these algorithms have not been applied to problems with functional expectation constraints. In this work, we propose the FW-CSOA algorithm that is not only projection-free but also achieves zero average constraint violation with <inline-formula><tex-math notation="LaTeX">$\mathcal{O}(T^{-\frac{1}{4}})$</tex-math></inline-formula> decay of the optimality gap. The efficacy of the proposed algorithms is tested on two relevant problems: fair classification and structured matrix completion.
Transparent mirror effect in twist-angle-disordered bilayer graphene

When light is incident on a medium with spatially disordered index of refraction, interference effects lead to near-perfect reflection when the number of dielectric interfaces is large, so that the medium becomes a "transparent mirror. " We investigate the analog of this effect for electrons in twisted bilayer graphene (TBG), for which local fluctuations of the twist angle give rise to a spatially random Fermi velocity. In a description that includes only spatial variation of Fermi velocity, we derive the incident-angle-dependent localization length for the case of quasi-one-dimensional disorder by mapping this problem onto one dimensional Anderson localization. The localization length diverges at normal incidence as a consequence of Klein tunneling, leading to a power-law decay of the transmission when averaged over incidence angle. In a minimal model of TBG, the modulation of twist angle also shifts the location of the Dirac cones in momentum space in a way that can be described by a random gauge field, and thus Klein tunneling is inexact. However, when the Dirac electron's incident momentum is large compared to these shifts, the primary effect of twist disorder is only to shift the incident angle associated with perfect transmission away from zero. These results suggest a mechanism for disorder-induced collimation, valley filtration, and energy filtration of Dirac electron beams, so that TBG offers a promising new platform for Dirac fermion optics.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?

Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has been addressed with a crowdsourcing-based approach to scale up labeling efforts: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of (non-expert) judges is exploited. We follow the same approach to study whether crowdsourcing is an effective and reliable method to assess statements truthfulness during a pandemic. We specifically target statements related to the COVID-19 health emergency, that is still ongoing at the time of the study and has arguably caused an increase of the amount of misinformation that is spreading online (a phenomenon for which the term "infodemic" has been used). By doing so, we are able to address (mis)information that is both related to a sensitive and personal issue like health and very recent as compared to when the judgment is done: two issues that have not been analyzed in related work. In our experiment, crowd workers are asked to assess the truthfulness of statements, as well as to provide evidence for the assessments as a URL and a text justification. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we also report results on many different aspects, including: agreement among workers, the effect of different aggregation functions, of scales transformations, and of workers background / bias. We also analyze workers behavior, in terms of queries submitted, URLs found / selected, text justifications, and other behavioral data like clicks and mouse actions collected by means of an ad hoc logger.
Reinforcement Learning with Trajectory Feedback

The standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair. However, in practice, it is often the case that such frequent feedback is not available. In this work, we take a first step towards relaxing this assumption and require a weaker form of feedback, which we refer to as \emph{trajectory feedback}. Instead of observing the reward obtained after every action, we assume we only receive a score that represents the quality of the whole trajectory observed by the agent, namely, the sum of all rewards obtained over this trajectory. We extend reinforcement learning algorithms to this setting, based on least-squares estimation of the unknown reward, for both the known and unknown transition model cases, and study the performance of these algorithms by analyzing their regret. For cases where the transition model is unknown, we offer a hybrid optimistic-Thompson Sampling approach that results in a tractable algorithm.
Valuation of Some Aromatic Rice (Oryza sativa L.) Genetic Materials to Achieve Tolerant Resources for Rice Stem Borers (Lepidoptera: Pyralidae)

For selection of aromatic rice (Oryza sativa L.) lines, fifteen (15) genotypes (Graminales: Poaceae), were evaluated for insect pests detection and agronomic characteristic through field test. The rice stem borer incidence was recorded according to deadhearts, whiteheads and grain yield parameters from the natural pest infestation. In general, the results on abundance and diversity of pest's species at different stages of crop growth revealed the yellow stem borer, Scirpophaga incertulas (Walker), (Lepidoptera: Pyralidae), as most dominant insect pest. On the basis of percent damage, none at all rice genotypes were totally free from stem borer damage and results showed significant differences (P< 0.05) for the selected traits. These results demonstrated that evaluation of rice genotypes conducted for stem borer tolerance showed resistance reactions by the genomes, viz., Basmati-15-15, Jajai-25/A, Basmati-15-13 and CR-33. These four genotypes possessed better antixenosis against adult egg laying and antibiosis for larval survival of stem borer as compared to the susceptible genome. These genotypes can be used in resistance development breeding program and should be admired in stem borer endemic areas. But, the genotypes Basmati- 20-300, Basmati-20-400, Basmati-1.5-3 and CR-12 were highly susceptible to borer's incidence with a corresponding reduction of paddy yield. The present study implies that the tolerant elite lines are able to minimize yellow stem borer's damage and could be readily used in breeding program to release aromatic rice varieties with significantly improved paddy yield.
Attention-based Fully Gated Conventional Recurrent Neural Network for Russian Handwritten Text

This research approaches the task of handwritten text with attention encoder-decoder networks that are trained on Kazakh and Russian language. We developed a novel deep neural network model based on Fully Gated CNN, supported by Multiple bidirectional GRU and Attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER) and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Also, we propose fully gated layers by taking the advantage of multiple the output feature from Tahn and input feature, this proposed work achieves better results and We experimented with our model on the Handwritten Kazakh & Russian Database (HKR). Our research is the first work on the HKR dataset and demonstrates state-of-the-art results to most of the other existing models.
Excluded-volume model for quarkyonic matter: Three-flavor baryon-quark mixture

The single-flavor excluded-volume model based on the effective size of baryons reproduces the hard-soft density evolution of the equation of state (EoS) required by the recent studies of GW170817. This phenomenological model basically realizes the concept of quarkyonic matter which is introduced from large-$N_{c}$ gauge theory for dense matter. Enhanced nucleon interactions and dynamically generated quark degrees of freedom can reproduce the hard-soft evolution of the EoS. In this paper, we extend the excluded-volume model to a three-flavor system by considering electromagnetic charge and possible weak equilibrium in order to obtain a proper description for the hard-soft behavior of the EoS inferred from the gravitational waves observations.
A PDE approach for open-loop equilibriums in time-inconsistent stochastic optimal control problems

This paper studies open-loop equilibriums for a general class of time-inconsistent stochastic control problems under jump-diffusion SDEs with deterministic coefficients. Inspired by the idea of Four-Step-Scheme for forward-backward stochastic differential equations with jumps (FBSDEJs, for short), we derive two systems of integro-partial differential equations (IPDEs, for short). Then, we rigorously prove a verification theorem which provides a sufficient condition for open-loop equilibrium strategies. As an illustration of the general theory, we discuss a mean-variance portfolio selection problem under a jump-diffusion model.
Contraceptive use among sexually active women living with HIV in western Ethiopia

Introduction Contraception can help to meet family planning goals for women living with HIV (WLHIV) as well as to support the prevention of mother to child transmission of HIV (PMTCT). However, there is little research into the contraceptive practice among sexually active WLHIV in Ethiopia. Therefore, we aimed to examine contraceptive practice among sexually active WLHIV in western Ethiopia and identify the factors that influenced such practice using the Health Belief Model (HBM). Methods A facility-based cross-sectional survey of 360 sexually active WLHIV was conducted from 19th March to 22nd June 2018 in western Ethiopia. The eligible participants were WLHIV aged between 18 and 49 years who reported being fecund and sexually active within the previous six months but were not pregnant and not wanting to have another child within two years. Modified Poisson regression analyses were conducted to identify factors that influenced contraceptive practice among sexually active WLHIV in western Ethiopia. Results Among sexually active WLHIV (n = 360), 75% used contraception with 25% having unmet needs. Of the contraceptive users, 44.8% used injectables, 37.4% used condoms and 28.5% used implants. Among 152 recorded births in the last five years, 17.8% were reported as mistimed and 25.7% as unwanted. Compared to WLHIV having no child after HIV diagnosis, having two or more children after HIV diagnosis (Adjusted Prevalence Ratio [APR] = 1.31; 95%CI 1.09–1.58) was associated with increased risk of contraceptive practice. However, sexually active unmarried WLHIV (APR = 0.69; 95%CI 0.50–0.95) were less likely to use any contraception compared to their sexually active married counterparts. Importantly, high perceived susceptibility (APR = 1.49; 95%CI 1.20–1.86) and medium perceived susceptibility (APR = 1.55; 95%CI 1.28–1.87) towards unintended pregnancy were associated with higher risk of contraceptive use than WLHIV with low perceived susceptibility. Conclusions Although contraceptive use amongst sexually active WLHIV was found to be high, our findings highlight the need for strengthening family planning services given the high rate of unintended pregnancies, the high rate of unmet needs for contraception, as well as the lower efficacy with some of the methods. Our findings also suggest that the HBM would be a valuable framework for healthcare providers, programme planners and policymakers to develop guidelines and policies for contraceptive counselling and choices.
A Review on Currently Available Potential Therapeutic Options for COVID-19

Abstract A series of unexplained pneumonia cases currently were first reported in December 2019 in Wuhan, China. Official names have been announced for the virus responsible, previously known as “2019 novel coronavirus” and the diseases it causes are, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease (COVID-19), respectively. Despite great efforts worldwide to control SARS-CoV-2, the spread of the virus has reached a pandemic. Infection prevention and control of this virus is the primary concern of public health officials and professionals. Currently, several therapeutic options for COVID-19 are proposed and vaccine development has been initiated for prevention purposes. In this review, we will discuss the most recent evidence about the current potential treatment options including anti-inflammatory drugs, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, nucleoside analogs, protease inhibitors, monoclonal antibodies, and convalescent plasma therapy. Some other agents such as vitamin D and melatonin, which were recommended as potential adjuvant treatments for COVID-19 infection are also presented. Moreover, the potential use of convalescent plasma for treatment of COVID-19 infection was described. Furthermore, in the next part of the current review, various vaccination approaches against COVID-19 including whole virus vaccines, recombinant subunit vaccine, DNA vaccines, and mRNA vaccines are discussed.