Torres-Castanedo, Carlos G.; Li, Kuang-Hui; Li, Xiaohang(arXiv, 2019-06-17)[Preprint]
Because of relatively low electron mobility of Ga2O3, it is important to identify proper current spreading materials. Fluorine-doped SnO2 (FTO) offers superior properties to those of indium tin oxide (ITO) including higher thermal stability, larger bandgap, and lower cost. However, the Ga2O3/FTO heterojunction including the important band offset and the I-V characteristics have not been reported. In this work, we have grown the Ga2O3/FTO heterojunction and performed X-ray photoelectron spectroscopy (XPS) measurement. The conduction and valence band offsets were determined to be 0.11 and 0.42 eV, indicating a minor barrier for electron transport and type-I characteristics. The subsequent I-V measurement of the Ga2O3/FTO heterojunction exhibited ohmic behavior. The results of this work manifests excellent candidacy of FTO for current spreading layers of Ga2O3 devices for high temperature and UV applications.
h-BN and Ga2O3 are two promising semiconductor materials. However, the band alignment of the Ga2O3/h-BN heterojunction has not been identified, hindering device development. In this study, the heterojunction was prepared by metalorganic chemical vapor deposition and pulsed laser deposition. Transmission electron microscopy confirmed sharp heterointerface and revealed structural evolution as amorphous-Ga2O3 grew thicker on lattice mismatched h-BN. The valence and conduction band offsets were determined by high-resolution X-ray photoemission spectroscopy to be 1.75 and 3.35-3.65 eV, respectively, corresponding to a type-II heterojunction. The extremely large type-II band offsets along with indirect bandgap of Ga2O3 may be leveraged for exceptional electron confinement and storage.
Arabidopsis is an important model organism and the first plant with its genome sequenced. Knowledge from studying this species has either direct or indirect applications to agriculture and human health. Quantitative proteomics by data-independent acquisition (SWATH/DIA-MS) was recently developed and considered as a high-throughput targetedlike approach for accurate proteome quantitation. In this approach, a high-quality and comprehensive library is a prerequisite. Here, we generated a protein expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We subsequently applied DIA-mass spectrometry using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups with 1,787 of them being differentially expressed which includes 65 proteins known to respond to abscisic acid stress. Mass spectrometry data are available via ProteomeXchange with identifier PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.
Wong, Aloysius Tze; Donaldson, Lara; Portes, Maria Teresa; Eppinger, Jörg; Feijó, José; Gehring, Christoph A(Cold Spring Harbor Laboratory, 2019-06-10)[Preprint]
Nitric oxide (NO) is a key signaling molecule that regulates diverse biological processes in both animals and plants. In animals, NO regulates vascular wall tone, neurotransmission and immune response while in plants, NO is essential for development and responses to biotic and abiotic stresses [1–3]. Interestingly, NO is involved in the sexual reproduction of both animals and plants mediating physiological events related to the male gamete [2, 4]. In animals, NO stimulates sperm motility  and binding to the plasma membrane of oocytes  while in plants, NO mediates pollen-stigma interactions and pollen tube guidance [6, 7]. NO generation in pollen tubes (PTs) has been demonstrated  and intracellular responses to NO include cytosolic Ca2+ elevation, actin organization, vesicle trafficking and cell wall deposition [7, 9]. However, the NO-responsive proteins that mediate these responses are still elusive. Here we show that PTs of Arabidopsis lacking the pollen-specific Diacylglycerol Kinase 4 (DGK4) grow slower and become insensitive to NO-dependent growth inhibition and re-orientation responses. Recombinant DGK4 protein yields NO-responsive spectral and catalytic changes in vitro which are compatible with a role in NO perception and signaling in PTs. NO is a fast, diffusible gas and, based on our results, we hypothesize it could serve in long range signaling and/or rapid cell-cell communication functions mediated by DGK4 downstream signaling during the progamic phase of angiosperm reproduction.
As groundwater is an essential nutrition and irrigation resource, its pollution may
lead to catastrophic consequences. Therefore, accurate modeling of the pollution of the soil and groundwater aquifer is highly important. As a model, we consider a density-driven groundwater flow problem with uncertain porosity and permeability. This problem may arise in geothermal reservoir simulation, natural saline-disposal basins, modeling of contaminant plumes, and subsurface flow.
This strongly nonlinear time-dependent problem describes the convection of the two-phase flow. This liquid streams under the gravity force, building so-called ``fingers''. The accurate numerical solution requires fine spatial resolution with an unstructured mesh and, therefore, high computational resources. Consequently, we run the parallelized simulation toolbox \myug with the geometric multigrid solver on Shaheen II supercomputer.
The parallelization is done in physical and stochastic spaces. Additionally, we demonstrate how the \myug toolbox can be run in a black-box fashion for testing different scenarios in the density-driven flow.
As a benchmark, we solve the Elder-like problem in a 3D domain. For approximations in the stochastic space, we use the generalized polynomial chaos expansion. We compute the mean, variance, and exceedance probabilities of the mass fraction. As a reference solution, we use the solution, obtained from the quasi-Monte Carlo method.
Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, such as regularized nonlinear acceleration (RNA) of Scieur et al., were proposed and shown to accelerate fixed point iterations. In contrast to RNA which computes extrapolation coefficients by (approximately) setting the gradient of the objective function to zero at the extrapolated point, we propose a more direct approach, which we call direct nonlinear acceleration (DNA). In DNA, we aim to minimize (an approximation of) the function value at the extrapolated point instead. We adopt a regularized approach with regularizers designed to prevent the model from entering a region in which the functional approximation is less precise. While the computational cost of DNA is comparable to that of RNA, our direct approach significantly outperforms RNA on both synthetic and real-world datasets. While the focus of this paper is on convex problems, we obtain very encouraging results in accelerating the training of neural networks.
Due to their hunger for big data, modern deep learning models are trained in parallel, often in distributed environments, where communication of model updates is the bottleneck. Various update compression (e.g., quantization, sparsification, dithering) techniques have been proposed in recent years as a successful tool to alleviate this problem. In this work, we introduce a new, remarkably simple and theoretically and practically effective compression technique, which we call natural compression (NC). Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two. NC is "natural" since the nearest power of two of a real expressed as a float can be obtained without any computation, simply by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by the tiny factor 9/8 only, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to 3-4x improvement in overall theoretical running time. For applications requiring more aggressive compression, we generalize NC to natural dithering, which we prove is exponentially better than the immensely popular random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect. Finally, we show that N is particularly effective for the in-network aggregation (INA) framework for distributed training, where the update aggregation is done on a switch, which can only perform integer computations.
We further develop a simple modification of Runge--Kutta methods that guarantees conservation or stability with respect to any inner-product norm. The modified methods can be explicit and retain the accuracy and stability properties of the unmodified Runge--Kutta method. We study the properties of the modified methods and show their effectiveness through numerical examples, including application to entropy-stability for first-order hyperbolic PDEs.
The framework of inner product norm preserving relaxation Runge-Kutta methods (David I. Ketcheson, Relaxation Runge-Kutta Methods: Conservation and Stability for Inner-Product Norms, 2019. arXiv: 1905.09847 [math.NA]) is extended to general convex quantities. Conservation, dissipation, or other solution properties with respect to any convex functional are enforced by the addition of a relaxation parameter that multiplies the Runge-Kutta update at each step. Moreover, other desirable stability (such as strong stability preservation) and efficiency (such as low storage requirements) properties are preserved. The technique can be applied to both explicit and implicit Runge-Kutta methods and requires only a small modification to existing implementations. The computational cost at each step is the solution of one additional scalar algebraic equation for which a good initial guess is available. The effectiveness of this approach is proved analytically and demonstrated in several numerical examples, including applications to high-order entropy-conservative and entropy-stable semi-discretizations on unstructured grids for the compressible Euler and Navier-Stokes equations.
Acinas, Silvia G.; Sánchez, Pablo; Salazar, Guillem; Cornejo-Castillo, Francisco M.; Sebastián, Marta; Logares, Ramiro; Sunagawa, Shinichi; Hingamp, Pascal; Ogata, Hiroyuki; Lima-Mendez, Gipsi; Roux, Simon; González, José M.; Arrieta, Jesús M.; Alam, Intikhab S.; Kamau, Allan; Bowler, Chris; Raes, Jeroen; Pesant, Stéphane; Bork, Peer; Agusti, Susana; Gojobori, Takashi; Bajic, Vladimir B.; Vaqué, Dolors; Sullivan, Matthew B.; Pedrós-Alió, Carlos; Massana, Ramon; Duarte, Carlos M.; Gasol, Josep M.(Cold Spring Harbor Laboratory, 2019-05-14)[Preprint]
The deep sea, the largest compartment of the ocean, is an essential component of the Earth system, but the functional exploration of its microbial communities lags far behind that of other marine realms. Here we analyze 58 bathypelagic microbial metagenomes from the Atlantic, Indian, and Pacific Oceans in an unprecedented sampling effort from the Malaspina Global Expedition, to resolve the metabolic architecture of the deep ocean microbiome. The Malaspina Deep-Sea Gene Collection, 71% of which consists of novel genes, reveals a strong dichotomy between the functional traits of free-living and particle-attached microorganisms, and shows relatively patchy composition challenging the paradigm of a uniform dark ocean ecosystem. Metagenome Assembled Genomes uncovered 11 potential new phyla, establishing references for deep ocean microbial taxa, and revealed mixotrophy to be a widespread trophic strategy in the deep ocean. These results expand our understanding of the functional diversity, metabolic versatility, and carbon cycling in the largest ecosystem on Earth.
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