Development of low modulus material for use in ceramic gas path seal applications

Three candidate materials were examined: Brunsbond (R) Pad; plasma sprayed porous NiCrAlY; and plasma sprayed low modulus microcracked zirconia. Evaluation consisted of mechanical, thermophysical, and oxidation resistance testing along with optical microscopy and a feasibility demonstration of attaching the material to a suitable substrate. The goals of the program were the following: feasibility of fastening or depositing the low modulus system onto a broad range of substrate alloys; feasibility of depositing or forming the low modulus system to a thickness of 0.19 cm to 0.38 cm; potential to attain a modulus of elasticity in the range of 3.4 to 6.9 GPa (0.5 to 1.0 MSI), and an ultimate strength of 17.2 MPa (2.5 ksi); suitable thermal conductivity; and static oxidation life of at least 1000 hours at 1311 K. The results of the program indicate that all three systems offer attractive properties as a strain isolator material

A new method for monitoring global volcanic activity

The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. While prediction of specific eruptions is still an evasive goal, early warning of a reawakening of quiescent volcanoes is now a distinct possibility. A prototypical global volcano surveillance system was established under the ERTS program. Instruments were installed in cooperation with local scientists on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador and Nicaragua. The sensors include 19 seismic event counters that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcano activity at many different volcanoes. Furthermore, these parameters can be measured relatively easily with new instrumentation

Development and evaluation of a prototype global volcano surveillance system utilizing the ERTS-1 satellite data collection system

There are no author-identified significant results in this report

Prismane C_8: A New Form of Carbon?

Our numerical calculations on small carbon clusters point to the existence of a metastable three-dimensional eight-atom cluster C$_8$ which has a shape of a six-atom triangular prism with two excess atoms above and below its bases. We gave this cluster the name "prismane". The binding energy of the prismane equals to 5.1 eV/atom, i.e., is 0.45 eV/atom lower than the binding energy of the stable one-dimensional eight-atom cluster and 2.3 eV/atom lower than the binding energy of the bulk graphite or diamond. Molecular dynamics simulations give evidence for a rather high stability of the prismane, the activation energy for a prismane decay being about 0.8 eV. The prismane lifetime increases rapidly as the temperature decreases indicating a possibility of experimental observation of this cluster.Comment: 5 pages (revtex), 3 figures (eps

When to cut corn

The object of the following experiment is to determine the proper time to cut corn, so as to get the most profitable returns from the crop. The ear is the most valuable part of the corn crop for the western farmer, but there is great value in the stover. Hence, it is important to know whether we can obtain both these values in full, or whether obtaining full value of the one necessitates a decrease in the value of the other. We selected twelve rows of Learning corn, of even quality and quantity, and long enough to make four shocks twelve hills square. These shock squares were laid off September 20th, and twenty stalks of even ripeness and size were selected and labeled on each square, that we might have samples that would represent the progress of ripening as nearly as possible

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Analysis of reaction and timing attacks against cryptosystems based on sparse parity-check codes

In this paper we study reaction and timing attacks against cryptosystems based on sparse parity-check codes, which encompass low-density parity-check (LDPC) codes and moderate-density parity-check (MDPC) codes. We show that the feasibility of these attacks is not strictly associated to the quasi-cyclic (QC) structure of the code but is related to the intrinsically probabilistic decoding of any sparse parity-check code. So, these attacks not only work against QC codes, but can be generalized to broader classes of codes. We provide a novel algorithm that, in the case of a QC code, allows recovering a larger amount of information than that retrievable through existing attacks and we use this algorithm to characterize new side-channel information leakages. We devise a theoretical model for the decoder that describes and justifies our results. Numerical simulations are provided that confirm the effectiveness of our approach

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Choosing Union Representation: The Role of Attitudes and Emotions

In the United States, most unions are recognized by a majority vote of employees through union representation elections administered by the government. Most empirical studies of individual voting behavior during union representation elections use a rational choice model. Recently, however, some have posited that voting is often influenced by emotions. We evaluate competing hypotheses about the determinants of union voting behavior by using data collected from a 2010 representation election at Delta Air Lines, a US-based company. In addition to the older rational choice framework, multiple regression results provide support for an emotional choice model. Positive feelings toward the employer are statistically significantly related to voting ‘no’ in a representation election, while positive feelings toward the union are related to a ‘yes’ vote. Effect sizes for the emotion variables were generally larger than those for the rational choice variables, suggesting that emotions may play a key role in representation election outcomes