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The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proven far too non-linear for human researchers. From a fundamental perspective, our understanding can be helped by the way in which machine learning models can rapidly discover complex interactions in large data sets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to ~daily), moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we advance proposals for the community in terms of research design, training, best practices, and support for synthetic diagnostics and data analysis., 14 pages, 4 figures. This work was the result of a meeting at the Lorentz Center, University of Leiden, 13th-17th January 2020. This is a preprint of Hatfield et al., Nature, 593, 7859, 351-361 (2021) https://www.nature.com/articles/s41586-021-03382-w

White dwarfs represent the final state of evolution for most stars1–3. Certain classes of white dwarfs pulsate4,5, leading to observable brightness variations, and analysis of these variations with theoretical stellar models probes their internal structure. Modelling of these pulsating stars provides stringent tests of white dwarf models and a detailed picture of the outcome of the late stages of stellar evolution6. However, the high-energy-density states that exist in white dwarfs are extremely difficult to reach and to measure in the laboratory, so theoretical predictions are largely untested at these conditions. Here we report measurements of the relationship between pressure and density along the principal shock Hugoniot (equations describing the state of the sample material before and after the passage of the shock derived from conservation laws) of hydrocarbon to within five per cent. The observed maximum compressibility is consistent with theoretical models that include detailed electronic structure. This is relevant for the equation of state of matter at pressures ranging from 100 million to 450 million atmospheres, where the understanding of white dwarf physics is sensitive to the equation of state and where models differ considerably. The measurements test these equation-of-state relations that are used in the modelling of white dwarfs and inertial confinement fusion experiments7,8, and we predict an increase in compressibility due to ionization of the inner-core orbitals of carbon. We also find that a detailed treatment of the electronic structure and the electron degeneracy pressure is required to capture the measured shape of the pressure–density evolution for hydrocarbon before peak compression. Our results illuminate the equation of state of the white dwarf envelope (the region surrounding the stellar core that contains partially ionized and partially degenerate non-ideal plasmas), which is a weak link in the constitutive physics informing the structure and evolution of white dwarf stars9. Researchers have measured the equation of state of hydrocarbon in a high-density regime, which is necessary for accurate modelling of the oscillations of white dwarf stars.

Severe price spikes of the major grain commodities and rapid expansion of cultivated area in the past two decades are symptoms of a severely stressed global food supply. Scientific discovery and improved agricultural productivity are needed and are enabled by unencumbered access to, and use of, genetic sequence data. In the same way the world witnessed rapid development of vaccines for COVID-19, genetic sequence data afford enormous opportunities to improve crop production. In addition to an enabling regulatory environment that allowed for the sharing of genetic sequence data, robust funding fostered the rapid development of coronavirus diagnostics and COVID-19 vaccines. A similar level of commitment, collaboration, and cooperation is needed for agriculture.

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes , we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

We adopt a technique, known in the machine learning community as transfer learning, to reduce the bias of computer simulation using very sparse experimental data. Unlike the Bayesian calibration, which is commonly used to estimate the simulation bias, the transfer learning approach discussed in this article involves calculating an artificial neural network surrogate model of the simulations. Assuming that the simulation code correctly predicts the trends in the experimental data but it is subject to unknown biases, we then partially retrain, or transfer learn, the initial surrogate model to match the experimental data. This process eliminates the bias while still taking advantage of the physics relations learned from the simulation. Transfer learning can be easily adapted to a wide range of problems in science and engineering. In this article, we carry out numerical tests to investigate the applicability of this technique to predict the observable outcomes of inertial confinement fusion (ICF) experiments under new conditions. Using our synthetic validation data set, we demonstrate that an accurate predictive model can be built by retraining an initial surrogate model with experimental data volumes so small that they are relevant to the ICF problem. This opens up new opportunities for knowledge transfer and building predictive models in physics. After implementing transfer learning in a standard neural network, we successfully extended the method to a more complex, generative adversarial network architecture, which will be needed for predicting not only scalars but also diagnostic images in our future work.

The mergers of some of the world's largest agribusinesses have led to speculation about what sort of global citizens the new companies will become and whether vulnerable rural populations, especially smallholder men and women farmers, will be negatively impacted. As innovation leaders in the agriculture industry, these new companies will be expected to play key roles in finding solutions for major agricultural challenges facing the world today. The private sector has a unique voice and responsibility to help bridge the innovation gap and ensure that good science reaches those countries where public investment in agricultural research is a low priority. In this paper, we review the obstacles facing agriculture over the next few decades, the role of agricultural innovation in overcoming those obstacles, and the need for greater public funding for agricultural research. We discuss how science-based solutions that drive revenue for industry can also advance agriculture in developing economies. Expediting agricultural innovation as well as increasing access to those benefits requires a different way of thinking about the sharing of technology to improve the lives of smallholder farmers and create a more equitable playing field for women in agriculture.

Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Time-resolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 PPa (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. The techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petapascal range, and can be used to deduce the energy flow in a wide variety of implosion systems to petapascal energy densities.

Time-resolved radiography can be used to obtain absolute shock Hugoniot states by simultaneously measuring at least two mechanical parameters of the shock, and this technique is particularly suitable for one-dimensional converging shocks where a single experiment probes a range of pressures as the converging shock strengthens. However, at sufficiently high pressures, the shocked material becomes hot enough that the x-ray opacity falls significantly. If the system includes a Lagrangian marker such that the mass within the marker is known, this additional information can be used to constrain the opacity as well as the Hugoniot state. In the limit that the opacity changes only on shock heating, and not significantly on subsequent isentropic compression, the opacity of the shocked material can be determined uniquely. More generally, it is necessary to assume the form of the variation of opacity with isentropic compression or to introduce multiple marker layers. Alternatively, assuming either the equation of state or the opacity, the presence of a marker layer in such experiments enables the non-assumed property to be deduced more accurately than from the radiographic density reconstruction alone. An example analysis is shown for measurements of a converging shock wave in polystyrene at the National Ignition Facility.

Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes., The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process. We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. LBANN combines multiple levels of parallelism and exploits some of the worlds largest supercomputers. We demonstrate our framework by creating a complex predictive model based on multi-variate data from high-energy-density physics containing hundreds of millions of images and hundreds of millions of scalar values derived from tens of millions of simulations of inertial confinement fusion. Our approach combines an HPC workflow and extends LBANN with optimized data ingestion and the new tournament-style training algorithm to produce a scalable neural network architecture using a CORAL-class supercomputer. Experimental results show that 64 trainers (1024 GPUs) achieve a speedup of 70.2 over a single trainer (16 GPUs) baseline, and an effective 109% parallel efficiency.

The equation of state (EOS) of materials at warm dense conditions poses significant challenges to both theory and experiment. We report a combined computational, modeling, and experimental investigation leveraging new theoretical and experimental capabilities to investigate warm-dense boron nitride (BN). The simulation methodologies include path integral Monte Carlo (PIMC), several density functional theory (DFT) molecular dynamics methods [plane-wave pseudopotential, Fermi operator expansion (FOE), and spectral quadrature (SQ)], activity expansion (actex), and all-electron Green's function Korringa-Kohn-Rostoker (mecca), and compute the pressure and internal energy of BN over a broad range of densities and temperatures. Our experiments were conducted at the Omega laser facility and the Hugoniot response of BN to unprecedented pressures (1200--2650 GPa). The EOSs computed using different methods cross validate one another in the warm-dense matter regime, and the experimental Hugoniot data are in good agreement with our theoretical predictions. By comparing the EOS results from different methods, we assess that the largest discrepancies between theoretical predictions are $\ensuremath{\lesssim}4%$ in pressure and $\ensuremath{\lesssim}3%$ in energy and occur at ${10}^{6}\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, slightly below the peak compression that corresponds to the $K$-shell ionization regime. At these conditions, we find remarkable consistency between the EOS from DFT calculations performed on different platforms and using different exchange-correlation functionals and those from PIMC using free-particle nodes. This provides strong evidence for the accuracy of both PIMC and DFT in the high-pressure, high-temperature regime. Moreover, the recently developed SQ and FOE methods produce EOS data that have significantly smaller statistical error bars than PIMC, and so represent significant advances for efficient computation at high temperatures. The shock Hugoniot predicted by PIMC, actex, and mecca shows a maximum compression ratio of $4.55\ifmmode\pm\else\textpm\fi{}0.05$ for an initial density of $2.26\phantom{\rule{0.28em}{0ex}}\mathrm{g}/{\mathrm{cm}}^{3}$, higher than the Thomas-Fermi predictions by about 5%. In addition, we construct tabular EOS models that are consistent with the first-principles simulations and the experimental data. Our findings clarify the ionic and electronic structure of BN over a broad range of temperatures and densities and quantify their roles in the EOS and properties of this material. The tabular models may be utilized for future simulations of laser-driven experiments that include BN as a candidate ablator material.

To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and high areal density at stagnation. The size of the driver and scale of the capsule required can be minimized by maintaining a high efficiency of energy coupling from the imploding shell to the hotspot. Significant 3D low mode asymmetries, however, are commonly observed in indirect-drive implosions and reduce the coupling of shell kinetic energy to the hotspot. To better quantify the magnitudes and impacts of shell density asymmetries, we have developed new analysis techniques and analytic models [Hurricane et al., Phys. Plasmas 27(6), 062704 (2020)]. To build confidence in the underlying data, we have also developed an analytic neutron transport model to cross-compare two independent measurements of asymmetry, which shows excellent agreement across shots for mode-1 (l = 1). This work also demonstrates that asymmetry can introduce potential sampling bias into down-scattered ratio measurements causing the solid-angle-average and uncertainty-weighted-average down-scattered ratios to differ significantly. Diagnosing asymmetries beyond mode-1 (l > 1) presents significant challenges. Using new diagnostic instruments and analysis techniques, however, evidence of significant Legendre mode P2 (l = 2, m = 0) and additional 3D asymmetries (l > 1, m ≠ 0) are beginning to emerge from the high precision activation diagnostic data (real-time nuclear activation detectors) and down-scattered neutron imaging data.

We examine the performance of pure boron, boron carbide, high density carbon, and boron nitride ablators in the polar direct drive exploding pusher (PDXP) platform. The platform uses the polar direct drive configuration at the National Ignition Facility to drive high ion temperatures in a room temperature capsule and has potential applications for plasma physics studies and as a neutron source. The higher tensile strength of these materials compared to plastic enables a thinner ablator to support higher gas pressures, which could help optimize its performance for plasma physics experiments, while ablators containing boron enable the possibility of collecting additional data to constrain models of the platform. Applying recently developed and experimentally validated equation of state models for the boron materials, we examine the performance of these materials as ablators in 2D simulations, with particular focus on changes to the ablator and gas areal density, as well as the predicted symmetry of the inherently 2D implosion.

Convergent high-energy-density (HED) experimental platforms are used to study matter under some of the most extreme conditions that can be produced on Earth, comparable to the interior of stars. There are many challenges in using these systems for fundamental measurements currently being addressed by new analysis methods, such as the combination of a reduced physics model and Bayesian inference, allowing a self-consistent inference of physical quantities with a robust error analysis. These methods in combination with simple (as compared to inertial confinement fusion implosions) implosion platforms, which can be modified to show sensitivity to different physical mechanisms of interest, are used to study the physical properties of matter under extreme conditions. This work discusses a subset of implosion targets for studying opacity effects, electron–ion equilibration, and thermal conductivity and, as an example, a system consisting of a thick-shelled, gas-filled laser-direct-drive implosion is used to show how a reduced model and Bayesian inference can help inform experimental design decisions such as diagnostic choice. It is shown that for this system that a combination of neutron and x-ray self-emission diagnostics is critical for constraining the details of the thermodynamic states in the system and that the conductivity exponent in a Spitzer like framework can be constrained to the 30% level in deuterium at gigabar conditions. This process can be applied to many HED systems to make underlying model assumptions explicit and facilitate experimental design and analysis.

Hybrid crops are underutilized in many developing countries. Subsistence farmers in sub-Saharan Africa (SSA) rely predominantly on outdated hybrids and open-pollinated varieties, which has limited the region's ability to achieve food security and agricultural sustainability goals. Key challenges in SSA include lack of access to improved hybrid seed, insufficient infrastructure to support a formal seed system, and limited smallholder farmer access to input and output markets. Implementing improved seed systems and creating greater market access will require engagement from the public and private sector and the governments within Africa. This paper reviews the importance of hybrids in agriculture, the challenges associated with creating new hybrids, and the technological advancements that will enable more efficient production of quality hybrids in Africa.

Open access to genetic sequence data, often referred to as Digital Sequence Information, has been available since genome sequencing became possible and creates both monetary and nonmonetary value. Nonmonetary value is created when scientists access sequence data for discovery, collaboration, and innovation. Monetary value is created when genetic variability is leveraged to develop more robust and resilient crop plants, vibrant seed systems, more sustainable agriculture, and food security for consumers. Millions of dollars have been invested in curating and creating access to sequence databases and scientists from almost every country in the world have accessed these databases, free of charge. This access may now be threatened by well-meaning policy-makers who have not consulted with the scientific community. Monetizing or creating greater regulation of genetic sequence data would create barriers to innovation, partnering, and problem-solving.

For many years, low mode asymmetry in inertially confined fusion (ICF) implosions has been recognized as a potential performance limiting factor, but analysis has been limited to using simulations and searching for data correlations. Herein, an analytically solvable model based upon the simple picture of an asymmetric piston is presented. Asymmetry of the shell driving the implosion, as opposed to asymmetry in the hot-spot, is key to the model. The model provides a unifying framework for the action of mode-1 shell asymmetry and the resulting connections between various diagnostic signatures. A key variable in the model is the shell asymmetry fraction, f, which is related to the areal density variation of the shell surrounding the hot-spot. It is shown that f is simply related to the observed hot-spot mode-1 velocity and to the concept of residual energy in an implosion. The model presented in this paper yields explicit expressions for the hot-spot diameter, stagnation pressure, hot-spot energy, inertial confinement-time, Lawson parameter, hot-spot temperature, and fusion yield under the action of mode-1 asymmetry. Agreement is found between the theory scalings when compared to ICF implosion data from the National Ignition Facility and to large ensembles of detailed simulations, making the theory a useful tool for interpreting data. The theory provides a basis for setting tolerable limits on asymmetry.

We describe the overall performance of the major indirect-drive inertial confinement fusion campaigns executed at the National Ignition Facility. With respect to the proximity to ignition, we can describe the performance of current experiments both in terms of no-burn ignition metrics (metrics based on the hydrodynamic performance of targets in the absence of alpha-particle heating) and in terms of the thermodynamic properties of the hotspot and dense fuel at stagnation—in particular, the hotspot pressure, temperature, and areal density. We describe a simple 1D isobaric model to derive these quantities from experimental observables and examine where current experiments lie with respect to the conditions required for ignition.

We report a theoretical equation of state (EOS) table for boron across a wide range of temperatures (5.1$\times$10$^4$-5.2$\times$10$^8$ K) and densities (0.25-49 g/cm$^3$), and experimental shock Hugoniot data at unprecedented high pressures (5608$\pm$118 GPa). The calculations are performed with full, first-principles methods combining path integral Monte Carlo (PIMC) at high temperatures and density functional theory molecular dynamics (DFT-MD) methods at lower temperatures. PIMC and DFT-MD cross-validate each other by providing coherent EOS (difference $, Comment: 12 pages, 9 figures, 2 tables

International audience; Material equation-of-state (EOS) models, generally providing the pressure and internal energy for a given density and temperature, are required to close the equations of hydrodynamics. As a result they are an essential piece of physics used to simulate inertial confinement fusion (ICF) implosions. Historically, EOS models based on different physical/chemical pictures of matter have been developed for ICF relevant materials such as the deuterium (D2) or deuterium-tritium (DT) fuel, as well as candidate ablator materials such as polystyrene (CH), glow-discharge polymer (GDP), beryllium (Be), carbon (C), and boron carbide (B4C). The accuracy of these EOS models can directly affect the reliability of ICF target design and understanding, as shock timing and material compressibility are essentially determined by what EOS models are used in ICF simulations. Systematic comparisons of current EOS models, benchmarking with experiments, not only help us to understand what the model differences are and why they occur, but also to identify the state-of-the-art EOS models for ICF target designers to use. For this purpose, the first Equation-of-State Workshop, supported by the US Department of Energy’s ICF program, was held at the Laboratory for Laser Energetics (LLE), University of Rochester on 31 May - 2nd June, 2017. This paper presents a detailed review on the findings from this workshop: (1) 5-10% model-model variations exist throughout the relevant parameter space, and can be much larger in regions where ionization and dissociation are occurring, (2) the D2 EOS is particularly uncertain, with no single model able to match the available experimental data, and this drives similar uncertainties in the CH EOS, and (3) new experimental capabilities such as Hugoniot measurements around 100 Mbar and high-quality temperature measurements are essential to reducing EOS uncertainty.

Author(s): Doeppner, T.; Swift, D.C.; Kritcher, A.L.; Bachmann, B.; Collins, G.W.; Chapman, D.A.; Hawreliak, J.; Kraus, D.; Nilsen, J.; Rothman, S.; Benedict, L.X.; Dewald, E.; Fratanduono, D.E.; Gaffney, J.A.; Glenzer, S.H.; Hamel, S.; Landen, O.L.; Lee, H.J.; LePape, S.; Ma, T.; MacDonald, M.J.; MacPhee, A.G.; Milathianaki, D.; Millot, M.; Neumayer, P.; Sterne, P.A.; Tommasini, R.; Falcone, R.W. | Abstract: We have developed an experimental platform for the National Ignition Facility that uses spherically converging shock waves for absolute equation-of-state (EOS) measurements along the principal Hugoniot. In this Letter, we present one indirect-drive implosion experiment with a polystyrene sample that employs radiographic compression measurements over a range of shock pressures reaching up to 60 Mbar (6 TPa). This significantly exceeds previously published results obtained on the Nova laser [R. Cauble et al., Phys. Rev. Lett. 80, 1248 (1998)] at a strongly improved precision, allowing us to discriminate between different EOS models. We find excellent agreement with Kohn-Sham density-functional-theory-based molecular dynamics simulations.

High-harmonic generation is a universal response of matter to strong femtosecond laser fields, coherently upconverting light to much shorter wavelengths. Optimizing the conversion of laser light into soft x-rays typically demands a trade-off between two competing factors. Because of reduced quantum diffusion of the radiating electron wave function, the emission from each species is highest when a short-wavelength ultraviolet driving laser is used. However, phase matching—the constructive addition of x-ray waves from a large number of atoms—favors longer-wavelength mid-infrared lasers.We identified a regime of high-harmonic generation driven by 40-cycle ultraviolet lasers in waveguides that can generate bright beams in the soft x-ray region of the spectrum, up to photon energies of 280 electron volts. Surprisingly, the high ultraviolet refractive indices of both neutral atoms and ions enabled effective phase matching, even in a multiply ionized plasma.We observed harmonics with very narrow linewidths, while calculations show that the x-rays emerge as nearly time-bandwidth–limited pulse trains of ~100 attoseconds., The experimental work was done at JILA, supported by Army Research Office grant WN11NF-13-1-0259, an NSF PFI AIR award, and U.S. Department of Energy (DOE) grant DE-SC0008803 (M.M.M., T.P., and H.C.K.). Theory was supported by a Marie Curie International Outgoing Fellowship within the EU Seventh Framework Programme for Research and Technological Development (2007–2013) under REA grant agreement 328334 (C.H.-G.); Junta de Castilla y León (SA116U13, UIC016) and MINECO (FIS2013-44174-P) (C.H.-G. and L.P.); NSF grants PHY-1125844 and PHY-1068706 and AFOSR MURI “Mathematical Modeling and Experimental Validation of Ultrafast Light-Matter Coupling associated with Filamentation in Transparent Media” grant FA9550-10-1-0561 (A.J.-B., R.J.L., X.G., A.L.G., M.M.M., and H.C.K.); Ministry of Science and Technology, Taiwan, grant 102-2112-M-007-025-MY3 (M.-C.C.); U.S. Department of Energy, Division of Chemical Sciences, Atomic, Molecular and Optical Sciences Program (A.B.); and DOE Office of Fusion Energy, HED Laboratory Plasmas program, grant AT5015033 (S.B.L., M.F., and J.A.G.). Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security LLC for DOE, National Nuclear Security Administration, under contract DE-AC52-07NA27344, LLNL-JRNL-676693. T.P., D.P., M.M.M., and H.C.K. have filed a patent on “Generation of VUV, EUV, X-ray Light Using VUV-UV-VIS Lasers,” U.S. patent application 61873794 (2013)/US 20150063385 (2015).

Maize (Zea mays L.) is among the most important grains contributing to global food security. Eighty years of genetic gain for yield of maize under both favorable and unfavorable stress-prone drought conditions have been documented for the US Corn Belt, yet maize remains vulnerable to drought conditions, especially at the critical developmental stage of flowering. Optimum AQUAmax (Dupont Pioneer) maize hybrids were developed for increased grain yield under drought and favorable conditions in the US Corn Belt. Following the initial commercial launch in 2011, a large on-farm data set has been accumulated (10,731 locations) comparing a large sample of the AQUAmax hybrids (78 hybrids) to a large sample of industry-leading hybrids (4287 hybrids) used by growers throughout the US Corn Belt. Following 3 yr (2011-2013) of on-farm industry-scale testing, the AQUAmax hybrids were on average 6.5% higher yielding under water-limited conditions (2006 locations) and 1.9% higher yielding under favorable growing conditions (8725 locations). In a complementary study, 3 yr (2010-2012) of hybrid-by-management-by-environment evaluation under water-limited conditions (14 locations) indicated that the AQUAmax hybrids had greater yield at higher plant populations when compared to non-AQUAmax hybrids. The combined results from research (2008-2010) and on-farm (2011-2013) testing throughout the US Corn Belt over the 6-yr period from 2008 to 2013 indicate that the AQUAmax hybrids offer farmers greater yield stability under water-limited conditions with no yield penalty when the water limitations are relieved and growing conditions are favorable.

The focus of the review paper is to discuss how biotechnological innovations are opening new frontiers to mitigate nutrition in key agricultural crops with potential for large-scale health impact to people in Africa. The general objective of the Africa Biofortified Sorghum (ABS) project is to develop and deploy sorghum with enhanced pro-vitamin A to farmers and end-users in Africa to alleviate vitamin A-related micronutrient deficiency diseases. To achieve this objective the project technology development team has developed several promising high pro-vitamin A sorghum events. ABS 203 events are so far the most advanced and well-characterised lead events with about 12 μg β-carotene/g tissue which would supply about 40–50 % of the daily recommended vitamin A at harvest. Through gene expression optimisation other events with higher amounts of pro-vitamin A, including ABS 214, ABS 235, ABS 239 with 25, 30–40, 40–50 μg β-carotene/g tissue, respectively, have been developed. ABS 239 would provide twice recommended pro-vitamin A at harvest, 50–90 % after 3 months storage and 13–45 % after 6 months storage for children. Preliminary results of introgression of ABS pro-vitamin A traits into local sorghum varieties in target countries Nigeria and Kenya show stable introgression of ABS vitamin A into local farmer-preferred sorghums varieties. ABS gene Intellectual Property Rights and Freedom to Operate have been donated for use royalty free for Africa. Prior to the focus on the current target countries, the project was implemented by fourteen institutions in Africa and the USA. For the next 5 years, the project will complete ABS product development, complete regulatory science data package and apply for product deregulation in target African countries.

One of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $$\mathrm{ND}^2\mathrm{AV}$$ND2AV: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $$\mathrm{ND}^2\mathrm{AV}$$ND2AV uses a modular design providing easy extensibility and customization for different applications. $$\mathrm{ND}^2\mathrm{AV}$$ND2AV is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.

We make direct observations of localized light absorption in a single nanostructure irradiated by a strong femtosecond laser field, by developing and applying a technique that we refer to as plasma explosion imaging. By imaging the photoion momentum distribution resulting from plasma formation in a laser-irradiated nanostructure, we map the spatial location of the highly localized plasma and thereby image the nanoscale light absorption. Our method probes individual, isolated nanoparticles in vacuum, which allows us to observe how small variations in the composition, shape, and orientation of the nanostructures lead to vastly different light absorption. Here, we study four different nanoparticle samples with overall dimensions of ∼100 nm and find that each sample exhibits distinct light absorption mechanisms despite their similar size. Specifically, we observe subwavelength focusing in single NaCl crystals, symmetric absorption in TiO2 aggregates, surface enhancement in dielectric particles containing a single gold nanoparticle, and interparticle hot spots in dielectric particles containing multiple smaller gold nanoparticles. These observations demonstrate how plasma explosion imaging directly reveals the diverse ways in which nanoparticles respond to strong laser fields, a process that is notoriously challenging to model because of the rapid evolution of materials properties that takes place on the femtosecond time scale as a solid nanostructure is transformed into a dense plasma.

The Guderley model of a self-similar imploding shock based on the group invariance of the flow equations is a powerful tool in understanding the behavior of converging shock waves. Two modifications described here improve the predictions of observable quantities in spherical-shock wave experiments. First, a noninfinite boundary condition is established by the isentropic release of the outer pressure. Second, a two-temperature system of ions and electrons allows description of higher temperatures while conserving energy and without perturbing the overall hydrodynamics of the solution. These modifications of the Guderley model improve the prediction of the observables in laser driven spherical shock experiments in reference to a one dimensional (1-D) hydrodynamics code.

Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in the laboratory. Building truly predictive models is a significant challenge, with the potential to greatly accelerate progress to high yield and ignition. One path to more predictive models is to use experimental data to update the underlying physics in a way that can be extrapolated to new experiments and regimes. We describe a statistical framework for the calibration of ICF simulations using data collected at the National Ignition Facility (NIF). We perform Bayesian inferences for a series of laser shots using an approach that is designed to respect the physics simulation as much as possible and then build a second model that links the individual-shot inferences together. We show that this approach is able to match multiple X-ray and neutron diagnostics for a whole series of NIF “BigFoot” shots. Within the context of 2D radiation hydrodynamic simulations, our inference strongly favors a significant reduction in fuel compression over other known degradation mechanisms (namely, hohlraum issues and engineering perturbations). This analysis is expanded using a multifidelity technique to pick fuel-ablator mix from several candidate causes of the degraded fuel compression (including X-ray preheat and shock timing errors). Finally, we use our globally calibrated model to investigate the extra laser drive energy that would be required to overcome the inferred fuel compression issues in NIF BigFoot implosions.

The United States is the world's largest exporter of major grain and oilseed crops. In the three-year period from 2008–2010, it produced 39% of global maize and 35% of global soybean and accounted for 49% and 46%, respectively, of total global exports in these commodities. It also contributed 17% of total global exports in wheat and 11% of total rice exports. A large disruption to U.S. production of these crops, as occurred during the U.S. drought of 2012, can have a substantial impact on international grain markets. In this opinion piece, we consider the severity of this drought event and the impact on grain prices in relation to previous droughts of similar magnitude and use this information to highlight priorities for global research on drought and crop productivity to help buffer against future climatic shocks to global food supply.

First principles microphysics models are essential to the design and analysis of high energy density physics experiments. Using experimental data to investigate the underlying physics is also essential, particularly when simulations and experiments are not consistent with each other. This is a difficult task, due to the large number of physical models that play a role, and due to the complex (and as a result, noisy) nature of the experiments. This results in a large number of parameters that make any inference a daunting task; it is also very important to consistently treat both experimental and prior understanding of the problem. In this paper we present a Bayesian method that includes both these effects, and allows the inference of a set of modifiers which have been constructed to give information about microphysics models from experimental data. We pay particular attention to radiation transport models. The inference takes into account a large set of experimental parameters and an estimate of the prior knowledge through a modified $\chi^{2}$ function, which is minimised using an efficient genetic algorithm. Both factors play an essential role in our analysis. We find that although there is evidence of inaccuracies in off-line calculations of X ray drive intensity and Ge $L$ shell absorption, modifications to radiation transport are unable to reconcile differences between 1D HYDRA simulations and the experiment.

Background: Introgression of transgenes from crops to their wild species may enhance the adaptive advantage and therefore the invasiveness of and weedy forms. The study evaluated the effect of Africa Biofortified Sorghum (ABS) genes from ABS event 188 on the vegetative and reproductive features of the F 2 populations derived from crosses with Sorghum bicolor subsp. drummondii . Results: F 1 populations were obtained from reciprocal crosses involving ABS event 188 and its null segregant with inbred weedy parents from S. bicolor subsp. drummondii . Four F 2 populations and four parental populations were raised in RCBD with 4 replications in a confined field plot for two seasons. Vegetative and reproductive traits were evaluated. The vigour shown in the F 2 populations from the reciprocal crosses involving ABS event 188 and S. bicolor subsp. drummondii was similar to that in the crosses involving the null segregant and S. bicolor subsp. drummondii . Differences in vegetative and reproductive parameters were observed between the parental controls and the F 2 populations. Examination of the above and below ground vegetative biomass showed lack of novel weedy related features like rhizomes. Conclusions: Therefore, release of crops with ABS 188 transgenes into cropping systems is not likely to pose a risk of conferring additional adaptive advantage in the introgressing populations. The interaction of ABS genes in weedy backgrounds will also not have an effect towards enhancing the weedy features in these populations. Normal 0 21 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US; mso-fareast-language:EN-US;}

Unresolved transition arrays (UTAs) are a method of approximating complex atomic physics in plasma opacity calculations, and as such are very important in modern plasma dynamic simulations. In this paper we use full atomic physics calculations to test various UTA models, paying particular attention to the lineshape and its effect on the mean opacity. We find that a Gaussian lineshape is sufficient provided that the line width is correctly determined. This width can be calculated using existing formulae, or approximated by neglecting correlations between term line energies and strengths or the selection rules on term - term transitions. We have quantified transition array narrowing due to correlations for a set of iron transitions and shown that a simple model for these incurs fairly large random errors. The neglect of the selection rules is also seen to result in random errors of up to an order of magnitude. These results may prove very useful in the future development of opacity codes, in particular those intended to run in line with hydrodynamic simulations.

Machine learning is finding increasingly broad applications in the physical sciences. This most often involves building a model relationship between a dependent, measurable output, and an associated set of controllable, but complicated, independent inputs. We present a tutorial on current techniques in machine learning—a jumping-off point for interested researchers to advance their work. We focus on deep neural networks with an emphasis on demystifying deep learning. We begin with background ideas in machine learning and some example applications from current research in plasma physics. We discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, and then advancing to more sophisticated deep learning methods. We also address unsupervised learning and techniques for reducing the dimensionality of input spaces. Along the way, we describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We describe classes of tasks—predicting scalars, handling images, and fitting time-series—and prepare the reader to choose an appropriate technique. We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help.

We combine an aerodynamic lens with a velocity-map-imaging spectrometer to make the first measurements of ultrafast dynamics in individual nanoplasmas. By using two laser pulses (800 and 400 nm) delayed by several picoseconds, we find that we can generate and control shock wave propagation in nanoplasmas, confirming a decade of theoretical predictions. Additionally, we observe pronounced asymmetries in the photoion angular distributions resulting from nanoparticles of different structure and composition, demonstrating the ability to observe nanoscale light absorption at laser intensities near the damage threshold.

In a novel experiment that images the momentum distribution of individual, isolated 100-nm-scale plasmas, we make the first experimental observation of shock waves in nanoplasmas. We demonstrate that the introduction of a heating pulse prior to the main laser pulse increases the intensity of the shock wave, producing a strong burst of quasi-monochromatic ions with an energy spread of less than 15%. Numerical hydrodynamic calculations confirm the appearance of accelerating shock waves, and provide a mechanism for the generation and control of these shock waves. This observation of distinct shock waves in dense plasmas enables the control, study, and exploitation of nanoscale shock phenomena with tabletop-scale lasers., 8 pages of manuscript, 9 pages of supplemental information, total 17 pages