Production of Ultrafine, High-purity Ceramic Powders Using the US Bureau of Mines Developed Turbomill

Turbomilling, an innovative grinding technology developed by the U.S. Bureau of Mines in the early 1960's for delaminating filler-grade kaolinitic clays, has been expanded into the areas of particle size reduction, material mixing, and process reaction kinetics. The turbomill, originally called an attrition grinder, has been used for particle size reduction of many minerals, including natural and synthetic mica, pyrophyllite, talc, and marble. In recent years, an all-polymer version of the turbomill has been used to produce ultrafine, high-purity, advanced ceramic powders such as SiC, Si3N4, TiB2, and ZrO2. In addition to particle size reduction, the turbomill has been used to produce intimate mixtures of high surface area powders and whiskers. Raw materials, TiN, AlN, and Al2O3, used to produce a titanium nitride/aluminum oxynitride (TiN/AlON) composite, were mixed in the turbomill, resulting in strength increases over samples prepared by dry ball milling. Using the turbomill as a leach vessel, it was found that 90.4 pct of the copper was extracted from the chalcopyrite during a 4-hour leach test in ferric sulfate versus conventional processing which involves either roasting of the ore for Cu recovery or leaching of the ore for several days

So You\u27re the Club Vice-President...

You, and all 4-H officers, are representatives. You represent not only the local group, but the whole 4-H program. Your skills and abilities, standards and ideals, grooming, speech, and even smiles represent 4-H’ers everywhere. Representing others is one of your most important responsi-bilities because it exists at all times—not just while you are at the 4-H meetings. Those who are not acquainted with 4-H, judge it by its officers.https://lib.dr.iastate.edu/extension_4h_pubs/1021/thumbnail.jp

Analysis of selected factors as predictors of surviving family members\u27 attitudes towards euthanasia

The purpose of this cross-sectional study was to analyze factors which may relate to surviving family members\u27 attitudes toward euthanasia and to determine their significance, if any. This research used data which were collected by telephone survey from a sampling frame comprised of adult surviving family members whose names were listed in the Knoxville News Sentinel between July 1997 and April 1998. One thousand, six hundred seventy eight adults were listed on the sampling frame. Three hundred forty nine persons were randomly selected from the population to ensure a 95% confidence level and a permissible error of ± .04. The response rate based on the number of persons completing the survey relative to the number in the sample was 38%. The response rate which took into consideration those in the sample who were noneligible and nonreachable was 85%. The survey instrument was comprised of three scales: a euthanasia preference scale, a general self-efficacy scale, and an intrinsic religious orientation scale. Additionally, respondents were asked to complete a demographics section. A pilot study was carried out using sixty persons randomly drawn from the sampling frame to assess the survey instrument. SPSS® was used to carry out an item analysis of the scales resulting in the following Cronbach Alpha values: euthanasia scale (76); self- efficacy scale (84); and, intrinsic religious orientation scale (.66). Data were analyzed using regression analysis in SPSS®. Following the data analysis, it was concluded that the correlation (-0.44 at p\u3c0.001) and regression model (p \u3c0.001) show that there is a significant inverse relationship between the euthanasia and intrinsic religious orientation scores in this study. However, relationships between other predictors did not exist or were not able to be tested in this study due to paucity of data in some data cells. In conclusion, within the limitations of this study, intrinsic religious orientation is a predictor of euthanasia preference among surviving immediate family members

Interpretation of High Energy String Scattering in terms of String Configurations

High energy string scattering at fixed momentum transfer, known to be dominated by Regge trajectory exchange, is interpreted by identifying families of string states which induce each type of trajectory exchange. These include the usual leading trajectory $\alpha(t)=\alpha^\prime t+1$ and its daughters as well as the ``sister'' trajectories $\alpha_m(t)=\alpha(t)/m-(m-1)/2$ and their daughters. The contribution of the sister $\alpha_m$ to high energy scattering is dominated by string excitations in the $m^{th}$ mode. Thus, at large $-t$, string scattering is dominated by wee partons, consistently with a picture of string as an infinitely composite system of ``constituents'' which carry zero energy and momentum.Comment: 14 pages, phyzzx, psfig required, Florida Preprint UFIFT-94-

{DAFormer}: {I}mproving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and then propose a novel UDA method, DAFormer, based on the benchmark results. The DAFormer network consists of a Transformer encoder and a multi-level context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting DAFormer to the source domain: While the Rare Class Sampling on the source domain improves the quality of pseudo-labels by mitigating the confirmation bias of self-training towards common classes, the Thing-Class ImageNet Feature Distance and a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer significantly improves the state-of-the-art performance by 10.8 mIoU for GTA->Cityscapes and 5.4 mIoU for Synthia->Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://github.com/lhoyer/DAFormer

Homiletics

Homiletics: The Environmental Crisis and Christian Responsibilit

MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

In unsupervised domain adaptation (UDA), a model trained on source data (e.g.synthetic) is adapted to target data (e.g. real-world) without access to targetannotation. Most previous UDA methods struggle with classes that have a similarvisual appearance on the target domain as no ground truth is available to learnthe slight appearance differences. To address this problem, we propose a MaskedImage Consistency (MIC) module to enhance UDA by learning spatial contextrelations of the target domain as additional clues for robust visualrecognition. MIC enforces the consistency between predictions of masked targetimages, where random patches are withheld, and pseudo-labels that are generatedbased on the complete image by an exponential moving average teacher. Tominimize the consistency loss, the network has to learn to infer thepredictions of the masked regions from their context. Due to its simple anduniversal concept, MIC can be integrated into various UDA methods acrossdifferent visual recognition tasks such as image classification, semanticsegmentation, and object detection. MIC significantly improves thestate-of-the-art performance across the different recognition tasks forsynthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA. Forinstance, MIC achieves an unprecedented UDA performance of 75.9 mIoU and 92.8%on GTA-to-Cityscapes and VisDA-2017, respectively, which corresponds to animprovement of +2.1 and +3.0 percent points over the previous state of the art.The implementation is available at https://github.com/lhoyer/MIC.<br

Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised and domain-adaptive semantic segmentation, which is enhanced by self-supervised monocular depth estimation (SDE) trained only on unlabeled image sequences. In particular, we utilize SDE as an auxiliary task comprehensively across the entire learning framework: First, we automatically select the most useful samples to be annotated for semantic segmentation based on the correlation of sample diversity and difficulty between SDE and semantic segmentation. Second, we implement a strong data augmentation by mixing images and labels using the geometry of the scene. Third, we transfer knowledge from features learned during SDE to semantic segmentation by means of transfer and multi-task learning. And fourth, we exploit additional labeled synthetic data with Cross-Domain DepthMix and Matching Geometry Sampling to align synthetic and real data. We validate the proposed model on the Cityscapes dataset, where all four contributions demonstrate significant performance gains, and achieve state-of-the-art results for semi-supervised semantic segmentation as well as for semi-supervised domain adaptation. In particular, with only 1/30 of the Cityscapes labels, our method achieves 92% of the fully-supervised baseline performance and even 97% when exploiting additional data from GTA. The source code is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth