Miracle-dz/UniFAR-FTUs huggingface dataset:
Weed Management with Fomesafen Preemergence in Glyphosate-Resistant Cotton1

Field studies were conducted in Alabama in 1998 and 1999 to evaluate fomesafen preemergence (PRE) in glyphosate-resistant cotton. Fomesafen (0.3 and 0.4 kg ai/ha), fluometuron (1.4 kg ai/ha), and pyrithiobac (0.05 kg ai/ha) were applied alone or in tank mixtures. Glyphosate (0.4 kg ae/ha) was applied postemergence over-the-top (POT) and postemergence directed (PD). Regardless of PRE treatment, POT followed by (fb) PD applications of glyphosate were necessary for greater than 82% sicklepod control at midseason. In the absence of glyphosate, fomesafen, and fomesafen-containing tank mixtures controlled common cocklebur and Ipomoea species 77 and 72%, respectively, 14 d after PD application. However, two applications of glyphosate were needed for >94% season-long control of common cocklebur and Ipomoea species because of continued germination throughout the growing season. Postemergence applications of glyphosate added a 1,000 kg/ha seed cotton yield increase to all PRE treatments at both locations. Common cocklebur, Ipomoea species, and sicklepod control was not significantly increased by the addition of fomesafen PRE tank mixtures fb glyphosate postemergence as compared with glyphosate postemergence only. Nomenclature: Fluometuron; fomesafen; glyphosate; pyrithiobac; common cocklebur, Xanthium strumarium L. #3 XANST; Ipomoea species # IPOSS; sicklepod, Senna obtusifolia (L.) Irwin and Barneby # CASOB; cotton, Gossypium hirsutum L. ‘Deltapine 655 BG/RR’, ‘Paymaster 1220 BG/ RR’. Additional index words: CASOB, fluometuron, fomesafen, glyphosate, glyphosate timing, pyrithiobac, residual herbicide, Senna obtusifolia, soil-applied herbicide, transgenic cotton, XANST, Xanthium strumarium. Abbreviations: fb, followed by; PD, postemergence directed; POT, postemergence over-the-top; PRE, preemergence; SRS, Syngenta Research Site; WGS, Wiregrass Research and Education Center.
Supernovae in Orion: The Missing Link in the Star-forming History of the Region

The Orion Complex is a notable star-forming region fragmented into several different populations with substantial differences in their phase space. I propose a model that attempts to explain the how the Complex has evolved into this current configuration. In the model presented here, the large-scale expansion can be attributable to a supernova that exploded 6 Myr ago. The remnants of this explosion can be seen as Barnard’s loop, as the center of the expansion is consistent with the geometrical center of the H ii bubble. This is similar to the H ii bubble and the ballistic expansion that is associated with λ Ori, a region that was also the site of an ancient supernova. Assuming that the Orion Complex originally formed as one long filament spanning from the bottom of Orion A to ψ2 Ori (or potentially as far as λ Ori), Barnard’s loop supernova could have split the cloud, which led to the formation of Orion C & D. Furthermore, the shockwave that propagated into the filament could have swept along the gas through several parsecs, which led to the formation of the singularly most massive cluster in the solar neighborhood, the Orion Nebula Cluster. I also discuss other related nearby events, such as the formation of the Monogem ring and various runaways that have been ejected from the Orion Complex.
CO Depletion in Protoplanetary Disks: A Unified Picture Combining Physical Sequestration and Chemical Processing

The gas-phase CO abundance (relative to hydrogen) in protoplanetary disks decreases by up to two orders of magnitude from its interstellar medium value of ∼10−4, even after accounting for freeze-out and photodissociation. Previous studies have shown that while local chemical processing of CO and the sequestration of CO ice on solids in the midplane can both contribute, neither of these processes appears capable of consistently reaching the observed depletion factors on the relevant timescale of 1–3 Myr. In this study, we model these processes simultaneously by including a compact chemical network (centered on carbon and oxygen) to 2D (r + z) simulations of the outer (r > 20 au) disk regions that include turbulent diffusion, pebble formation, and pebble dynamics. In general, we find that the CO/H2 abundance is a complex function of time and location. Focusing on CO in the warm molecular layer, we find that only the most complete model (with chemistry and pebble evolution included) can reach depletion factors consistent with observations. In the absence of pressure traps, highly efficient planetesimal formation, or high cosmic-ray ionization rates, this model also predicts a resurgence of CO vapor interior to the CO ice-line. We show the impact of physical and chemical processes on the elemental (C/O) and (C/H) ratios (in the gas and ice phases), discuss the use of CO as a disk mass tracer, and, finally, connect our predicted pebble ice compositions to those of pristine planetesimals as found in the Cold Classical Kuiper Belt and debris disks.
Network Learning Approaches to study World Happiness

The United Nations in its 2011 resolution declared the pursuit of happiness a fundamental human goal and proposed public and economic policies centered around happiness. In this paper we used 2 types of computational strategies viz. Predictive Modelling and Bayesian Networks (BNs) to model the processed historical happiness index data of 156 nations published by UN since 2012. We attacked the problem of prediction using General Regression Neural Networks (GRNNs) and show that it out performs other state of the art predictive models. To understand causal links amongst key features that have been proven to have a significant impact on world happiness, we first used a manual discretization scheme to discretize continuous variables into 3 levels viz. Low, Medium and High. A consensus World Happiness BN structure was then fixed after amalgamating information by learning 10000 different BNs using bootstrapping. Lastly, exact inference through conditional probability queries was used on this BN to unravel interesting relationships among the important features affecting happiness which would be useful in policy making.
Supplementary Materials for: “Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video”

Network Architecture. We present our network architecture using 3D Res50 backbone in Table 3. A similar architecture is also used for CSN-152 backbone. We followed [3] to use the features from bottom layers of the network for motor attention prediction and interaction hotspots estimation, and the features from the top layer for action anticipation. Our model jointly predicts motor attention, interaction hotspots and future actions, and thus is conceptually similar to multitask learning e.g., [4]. The key difference is that outputs of our model depends on each other. For example, motor attention is used for interaction hotspots estimation and both motor attention and interaction hotspots are used for action anticipation.
THE FORM, DISTRIBUTION AND SEASONAL ACCUMULATION OF CALCIUM IN KIWIFRUIT LEAVES.

Seasonal changes in the content of Ca and Mg in leaf blades collected from mature kiwifruit [Actinidia deliciosa (A. Chev.) C. F. Liang et A. R. Ferguson var. deliciosa] vines in a high-producing orchard were measured by a sequential fractionation procedure (14 M acetic acid, 0.25 M HC1 and residual). Total Ca and Mg concentrations decreased within the first 4 weeks of growth, but increased linearly thereafter to be 1322 and 197 μmol g-1 dry weight, respectively, by leaf fall. Total quantities of Ca and Mg accumulated at this time were approximately 2500 and 380 μmol per blade. Calcium oxalate, dissolved in the HC1 extract, was the predominant fraction at every harvest, accounting for up to 79 % of the total Ca content shortly after leaf emergence, and decreasing to 42 % at leaf fall. The quantity of physiologically active Ca (acetic acid-soluble) ranged from a minimum of 15 % (8 weeks after emergence) to a maximum of 41 % at the end of the season. Forms of Ca remaining in the solid residue after extraction made only a minor contribution (3 to 21 %) to the total content at any time during the season. In contrast, however, 89 to 96% of the total Mg was extracted by acetic acid at every harvest, with the remainder dissolved by HC1. Scanning electron microscopy and X-ray microanalysis of the lamina of mature leaves indicated the presence of two crystalline, Ca-rich products which differed in their morphology, location and distribution. Large styloids (120 to 270 μm in length) were concentrated in idioblasts adjacent to vascular tissue, while bundles of four-sided raphides, containing wedge-shaped apices, were located predominantly between the major transport vessels of the vascular tissue. X-ray diffraction analysis of crystalline isolates confirmed that both morphological forms are calcium oxalate monohydrate (whewellite).
Toxicity and bioavailability of mercury in spiked sediments on Hyalella curvispina Shoemaker, 1942

Mercury is a non–essential toxic heavy metal often found in bottom freshwater sediments. The aim of the present study was to evaluate the influence of the organic matter (OM) on the toxicity and bioavailability of mercury in sediment samples on the amphipod Hyalella curvispina, assessing survival and growth under chronic exposure. Species sensitivity in water–only tests was LC–50 of 96 h 0.025 mg/L. Results indicate that sediments with OM did not induce lethality under concentrations of mercury up to 10 mg/kg dw. On the contrary, exposed organisms to sediments without OM were significantly affected at half of that metal concentration. Sublethal effects were evident at 3 mg/kg. Presence and proportion of OM in sediment is clearly influencing mercury bioavailability, affecting toxicity in a different level according to the end point being assessed.
Undergraduate Education in Neuroscience: A Model for Interdisciplinary Study

to devise pedagogical strategies that would help our students rebound from an intellectual lethargy resulting from years of societal neglect. As recent publications in the popular press (2) and Science (3) illustrate, these efforts at reform have been wide-ranging and have penetrated science education at all levels. Two recent reports, in particular, may have a profound impact on the way science education proceeds in the United States: The National Education
Cheddar Cheese Starters: Current Knowledge and Practices of Phage Characteristics and Strain Selection

Few people will argue with the statement that the primary function of a starter is to produce lactic acid in the cheese vats. In any specific cheese plant, starter activity may be retarded by a number of factors. Inhibitory substances in the milk, either as a result of mastitis treatment or of the poor bacteriological quality of the raw milk, may stop acid production altogether. To a lesser extent, variation in the composition of the milk throughout the year and over-ripening of the starter in the bulk starter tanks also can reduce starter activity. However, the most important inhibitory factor by far is bacteriophage. A few years ago one of our older cheesemakers commented that he had been working in 1930 at the cheese plant where Whitehead first started his investigations on single s trains-"I was there", he said, "when Hugh Whitehead investigated this phage business. I told him no good would come of it". Whitehead, of course, quickly developed practical ways of controlling phage in plants, such as the use of sealed bulk starter tanks and chlorination of the vats before filling them with milk. Important as these were, however, the essential part of the New Zealand system always has been the careful selection of starter strains.
Optimal Bayesian estimation of Gaussian mixtures with growing number of components

We study posterior concentration properties of Bayesian procedures for estimating finite Gaussian mixtures in which the number of components is unknown and allowed to grow with the sample size. Under this general setup, we derive a series of new theoretical results. More specifically, we first show that under mild conditions on the prior, the posterior distribution concentrates around the true mixing distribution at a near optimal rate with respect to the Wasserstein distance. Under a separation condition on the true mixing distribution, we further show that a better and adaptive convergence rate can be achieved, and the number of components can be consistently estimated. Furthermore, we derive optimal convergence rates for the higher-order mixture models where the number of components diverges arbitrarily fast. In addition, we consider the fractional posterior and investigate its posterior contraction rates, which are also shown to be minimax optimal in estimating the mixing distribution under mild conditions. We also investigate Bayesian estimation of general mixtures with strong identifiability conditions, and derive the optimal convergence rates when the number of components is fixed. Lastly, we study theoretical properties of the posterior of the popular Dirichlet process (DP) mixture prior, and show that such a model can provide a reasonable estimate for the number of components while only guaranteeing a slow convergence rate of the mixing distribution estimation.
The Effect of Supernovae on the Turbulence and Dispersal of Molecular Clouds

We study the impact of supernovae on individual molecular clouds, using a high-resolution magnetohydrodynamic simulation of a 250 pc region where we resolve the formation of individual massive stars. The supernova feedback is implemented with real supernovae, meaning supernovae that are the natural evolution of the resolved massive stars, so their position and timing are self-consistent. We select a large sample of molecular clouds from the simulation to investigate the supernova energy injection and the resulting properties of molecular clouds. We find that molecular clouds have a lifetime of a few dynamical times, less than half of them contract to the point of becoming gravitationally bound, and the dispersal time of bound clouds of order one dynamical time is a factor of 2 shorter than that of unbound clouds. We stress the importance of internal supernovae, that is, massive stars that explode inside their parent cloud, in setting the cloud dispersal time, and their huge overdensity compared to models where the supernovae are randomly distributed. We also quantify the energy injection efficiency of supernovae as a function of supernova distance to the clouds. We conclude that intermittent driving by supernovae can maintain molecular cloud turbulence and may be the main process for cloud dispersal and that the full role of supernovae in the evolution of molecular clouds cannot be fully accounted for without a self-consistent implementation of the supernova feedback.
Exocomets from a Solar System Perspective

Exocomets are small bodies releasing gas and dust which orbit stars other than the Sun. Their existence was first inferred from the detection of variable absorption features in stellar spectra in the late 1980s using spectroscopy. More recently, they have been detected through photometric transits from space, and through far-IR/mm gas emission within debris disks. As (exo)comets are considered to contain the most pristine material accessible in stellar systems, they hold the potential to give us information about early stage formation and evolution conditions of extra solar systems. In the solar system, comets carry the physical and chemical memory of the protoplanetary disk environment where they formed, providing relevant information on processes in the primordial solar nebula. The aim of this paper is to compare essential compositional properties between solar system comets and exocomets to allow for the development of new observational methods and techniques. The paper aims to highlight commonalities and to discuss differences which may aid the communication between the involved research communities and perhaps also avoid misconceptions. The compositional properties of solar system comets and exocomets are summarized before providing an observational comparison between them. Exocomets likely vary in their composition depending on their formation environment like solar system comets do, and since exocomets are not resolved spatially, they pose a challenge when comparing them to high fidelity observations of solar system comets. Observations of gas around main sequence stars, spectroscopic observations of “polluted” white dwarf atmospheres and spectroscopic observations of transiting exocomets suggest that exocomets may show compositional similarities with solar system comets. The recent interstellar visitor 2I/Borisov showed gas, dust and nuclear properties similar to that of solar system comets. This raises the tantalising prospect that observations of interstellar comets may help bridge the fields of exocomet and solar system comets.
Progressive Multi-Scale Residual Network for Single Image Super-Resolution

Multi-scale convolutional neural networks (CNNs) achieve significant success in single image super-resolution (SISR), which considers the comprehensive information from different receptive fields. However, recent multi-scale networks usually aim to build the hierarchical exploration with different sizes of filters, which lead to high computation complexity costs, and seldom focus on the inherent correlations among different scales. This paper converts the multi-scale exploration into a sequential manner, and proposes a progressive multi-scale residual network (PMRN) for SISR problem. Specifically, we devise a progressive multi-scale residual block (PMRB) to substitute the larger filters with small filter combinations, and gradually explore the hierarchical information. Furthermore, channel- and pixel-wise attention mechanism (CPA) is designed for finding the inherent correlations among image features with weighting and bias factors, which concentrates more on high-frequency information. Experimental results show that the proposed PMRN recovers structural textures more effectively with superior PSNR/SSIM results than other small networks. The extension model PMRN$^+$ with self-ensemble achieves competitive or better results than large networks with much fewer parameters and lower computation complexity.
Large scale analysis of generalization error in learning using margin based classification methods

Large-margin classifiers are popular methods for classification. We derive the asymptotic expression for the generalization error of a family of large-margin classifiers in the limit of both sample size n and dimension p going to ∞ with fixed ratio α = n/p. This family covers a broad range of commonly used classifiers including support vector machine, distance weighted discrimination, and penalized logistic regression. Our result can be used to establish the phase transition boundary for the separability of two classes. We assume that the data are generated from a single multivariate Gaussian distribution with arbitrary covariance structure. We explore two special choices for the covariance matrix: spiked population model and two layer neural networks with random first layer weights. The method we used for deriving the closed-form expression is from statistical physics known as the replica method. Our asymptotic results match simulations already when n, p are of the order of a few hundreds. For two layer neural networks, we reproduce the recently observed ‘double descent’ phenomenology for several classification models. We also discuss some statistical insights that can be drawn from these analysis.
The Energy Complexity of BFS in Radio Networks

We consider a model of energy complexity in Radio Networks in which transmitting or listening on the channel costs one unit of energy and computation is free. This simplified model captures key aspects of battery-powered sensors: that battery-life is most influenced by transceiver usage, and that at low transmission powers, the actual cost of transmitting and listening are very similar. The energy complexity of tasks in single-hop (clique) networks are well understood [6, 9, 20, 32]. Recent work of Chang et al. [8] considered energy complexity in multi-hop networks and showed that Broadcast admits an energy-efficient protocol, by which we mean each of the n nodes in the network spends O(polylog(n)) energy. This work left open the strange possibility that all natural problems in multi-hop networks might admit such an energy-efficient solution. In this paper we prove that the landscape of energy complexity is rich enough to support a multitude of problem complexities. Whereas Broadcast can be solved by an energy-efficient protocol, exact computation of Diameter cannot, requiring Ω(n) energy. Our main result is that BreadthFirstSearch has sub-polynomial energy complexity at most [EQUATION]; whether it admits an efficient O(polylog(n))-energy protocol is an open problem. Our main algorithm involves recursively solving a generalized BFS problem on a "cluster graph" introduced by Miller, Peng, and Xu [31]. In this application, we make crucial use of a close relationship between distances in this cluster graph, and distances in the original network. This relationship is new and may be of independent interest. We also consider the problem of approximating the network Diameter. From our main result, it is immediate that Diameter can be 2-approximated using no(1) energy per node. We observe that, for all ∈ > 0, approximating Diameter to within a (2 − ∈) factor requires Ω(n) energy per node. However, this lower bound is only due to graphs of very small diameter; for large-diameter graphs, we prove that the diameter can be nearly 3/2-approximated using O(n1/2+o(1)) energy per node.
Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods

Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theoretical developments on inference after covariate-adaptive randomization are mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels utilized in randomization and a further adjusting for covariates not used in randomization, in this article we propose several estimators for model free inference on average treatment effect defined as the difference between response means under two treatments. We establish asymptotic normality of the proposed estimators under all popular covariate-adaptive randomization schemes including the minimization whose theoretical property is unclear, and we show that the asymptotic distributions are invariant with respect to covariate-adaptive randomization methods. Consistent variance estimators are constructed for asymptotic inference. Asymptotic relative efficiencies and finite sample properties of estimators are also studied. We recommend using one of our proposed estimators for valid and model free inference after covariate-adaptive randomization.