Disease resistance and productivity in genetically improved loblolly pine: Results from a resistance screening trial and a midrotation comparison of genetic improvement levels

Decades of tree improvement has resulted in genetic gains in loblolly pine productivity, form, and resistance to fusiform rust. The goal of this study was to advance the understanding and applied use of genetic improvement by analyzing inter- and intra-provenances hybrids’ rust resistance and evaluating midrotation performance of varying levels of genetically improved stock types. The first study compares 16 seedlots at the USDA Resistance Screening Center and evaluates rust resistance of controlled-pollinated inter- and intra-provenances crosses, and openpollinated seedlots from three provenances: Western Gulf, Atlantic Coastal, and Interior Piedmont. Post inoculation, one Coastal OP seedlot was resistant and ten of the seedlots were susceptible to the disease. The second study compares three levels of improved stock types: second-generation open-pollinated, controlled pollinated, and varietal material. After the fifteenth growing season, all three improved stock types were not significantly different from one another in defects, height, diameter, volume, and exhibited site index

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at https://github.com/Bartzi/loan

The 30-kW ammonia arcjet technology

The technical results are summarized of a 30 kW class ammonia propellant arcjet technology program. Evaluation of previous arcjet thruster performance, including materials analysis of used thruster components, led to the design of an arcjet with improved performance and thermal characteristics. Tests of the new engine demonstrated that engine performance is relatively insensitive to cathode tip geometry. Other data suggested a maximum sustainable arc length for a given thruster configuration, beyond which the arc may reconfigure in a destructive manner. A flow controller calibration error was identified. This error caused previously reported values of specific impulse and thrust efficiency to be 20 percent higher than the real values. Corrected arcjet performance data are given. Duration tests of 413 and 252 hours, and several tests 100 hours in duration, were performed. The cathode tip erosion rate increased with increasing arc current. Elimination of power source ripple did not affect cathode tip whisker growth. Results of arcjet modeling, diagnostic development and mission analyses are also discussed. The 30 kW ammonia arcjet may now be considered ready for development for a flight demonstration, but widespread application of 30 kW class arcjet will require improved efficiency and lifetime

Ruthenium/Iridium Ratios in the Cretaceous-tertiary Boundary Clay: Implications for Global Dispersal and Fractionation Within the Ejecta Cloud

Ruthenium (Ru) and iridium (Ir) are the least mobile platinum group elements (PGE's) within the Cretaceous-Tertiary (K-T) boundary clay (BC). The Ru/Ir ratio is, therefore, the most useful PGE interelement ratio for distinguishing terrestrial and extraterrestrial contributions to the BC. The Ru/Ir ratio of marine K-T sections (1.77 +/- 0.53) is statistically different from that of the continental sections (0.93 +/- 0.28). The marine Ru/Ir ratios are chondritic (C1 = 1.48 +/- 0.09), but the continental ratios are not. We discovered an inverse correlation of shocked quartz size (or distance from the impact site) and Ru/Ir ratio. This correlation may arise from the difference in Ru and Ir vaporization temperature and/or fractionation during condensation from the ejecta cloud. Postsedimentary alteration, remobilization, or terrestrial PGE input may be responsible for the Ru/Ir ratio variations within the groups of marine and continental sites studied. The marine ratios could also be attained if approximately 15 percent of the boundary metals were contributed by Deccan Trap emissions. However, volcanic emissions could not have been the principal source of the PGE's in the BC because mantle PGE ratios and abundances are inconsistent with those measured in the clay. The Ru/Ir values for pristine Tertiary mantle xenoliths (2.6 +/- 0.48), picrites (4.1 +/- 1.8), and Deccan Trap basalt (3.42 +/- 1.96) are all statistically distinct from those measured in the K-T BC

A B-Spline-Based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization

Airfoil aerodynamic optimization is of great importance in aircraft design; however, it relies on high-fidelity physics-based models that are computationally expensive to evaluate. In this work, we provide a methodology to reduce the computational cost for airfoil aerodynamic optimization. Firstly, we develop a B-spline based generative adversarial networks (BSplineGAN) parameterization method to automatically infer design space with sufficient shape variability. Secondly, we construct multi-layer neural network (MNN) surrogates for fast predictions on aerodynamic drag, lift, and pitching moment coefficients. The BSplineGAN has a relative error lower than 1% when fitting to UIUC database. Verification of MNN surrogates shows the root means square errors (RMSE) of all aerodynamic coefficients are within the range of 20%–40% standard deviation of testing points. Both normalized RMSE and relative errors are controlled within 1%. The proposed methodology is then demonstrated on an airfoil aerodynamic optimization. We also verified the baseline and optimized designs using a high-fidelity computational fluid dynamic solver. The proposed framework has the potential to enable web-based fast interactive airfoil aerodynamic optimization

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Fluid Inclusion Petrography and Microthermometry of the Middle Valley Hydrothermal System, Northern Juan de Fuca Ridge

Middle Valley is a hydrothermally active, sediment-covered rift at the northernmost end of the Juan de Fuca Ridge. Two hydrothermal centers are known from previous work: (1) a 60-m-high sediment mound with a 35-m-high inactive sulfide mound and two 20-m-high sulfide mounds 330 m to the south, one of which is known to be active, and (2) several mounds with attendant active hydrothermal chimneys. These sites (Sites 856 and 858, respectively), as well as other adjacent areas (Sites 857 and 855), were drilled during Leg 139 of the Ocean Drilling Program. Fluid inclusion petrographic observations and microthermometric measurements were made on a variety of samples and minerals recovered from these cores: (1) quartz from hydrothermally altered sediment; (2) low iron sphalerite and interstitial dolomite in massive sulfide; (3) calcite-sulfide veins cross-cutting sediment; (4) calcite and anhydrite concretions in sediment; (5) anhydrite veins cross-cutting sediment; and (6) wairakite and quartz veins cross-cutting mafic sills and sediment. Trapping temperatures of fluid inclusions in hydrothermal alteration minerals precipitated with massive sulfides range between 90° and 338°C. Fluid inclusions in calcite in carbonate concretions indicate these concretions formed between 112° and 192°C. Anhydrite in veins and concretions was precipitated between 137° and 311 °C. Quartz-wairakiteepidote veins in mafic sills and hydrothermally altered sediment were precipitated between 210° and 350°C. For all inclusions, there is a general increase in minimum trapping temperatures with increasing subsurface depth for all sites, with temperatures ranging from around 100°C at 2400 meters below sea level to around 275°C at 3100 mbsl. Eutectic and hydrohalite melting temperatures indicate that Ca, Na, and Cl are the dominant ionic species present in the inclusion fluids. Salinities for most inclusion fluids range between 2.5 and 7.0 equivalent weight percent NaCl. Most analyses are between 3 and 4.5 eq. wt% NaCl and similar to ambient bottom water, pore fluids, and vent fluid from Site 858. Trapped fluids are modified seawater, and there is no evidence for a significant magmatic fluid component. Oxygen isotopic compositions for fluids from which calcite concretions were precipitated, calculated from isotopic analyses of carbonates formed at low temperatures (133° to 158°C from fluid inclusions), are significantly enriched in 18O (δ1 8θ = +9.3‰ to +13.2‰), likely due to reaction with subsurface sediments at low water/rock ratios. Calcite that formed at higher temperatures (233°C) in hydrothermally altered sediment was precipitated from fluid only slightly enriched in 18O (δ1 8θ = +0.4%o). Estimated carbon isotope compositions of the fluid vary between δ13C = -7.0%e and -35.4‰ and are similar to the measured range for vent fluids

HoloDetect: Few-Shot Learning for Error Detection

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of two parts: (1) an expressive model to learn rich representations that capture the inherent syntactic and semantic heterogeneity of errors; and (2) a data augmentation model that, given a small seed of clean records, uses dataset-specific transformations to automatically generate additional training data. Our key insight is to learn data augmentation policies from the noisy input dataset in a weakly supervised manner. We show that our framework detects errors with an average precision of ~94% and an average recall of ~93% across a diverse array of datasets that exhibit different types and amounts of errors. We compare our approach to a comprehensive collection of error detection methods, ranging from traditional rule-based methods to ensemble-based and active learning approaches. We show that data augmentation yields an average improvement of 20 F1 points while it requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The code and synthetic networks generated are available upon request.Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.Medical Research Council (MRC)Epilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome TrustInnovate U