Explore the vast range of titles available.

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.

We present a new microstructure dataset consisting of ultrahigh carbon steel (UHCS) micrographs taken over a range of length scales under systematically varied heat treatments. Using the UHCS dataset as a case study, we develop a set of visualization tools for interacting with and exploring large microstructure and metadata datasets. Based on generic microstructure representations adapted from the field of computer vision, these tools enable image-based microstructure retrieval, as well as spatial maps of both microstructure and related metadata, such as processing conditions or properties measurements. We provide the microstructure image data, processing metadata, and source code for these microstructure exploration tools. The UHCS dataset is intended as a community resource for development and evaluation of microstructure data science techniques and for creation of microstructure data science teaching modules.

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly-available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov (https://materialsdata.nist.gov/handle/11256/964).

We introduce a microstructure informatics dataset focusing on complex, hierarchical structures found in a single Ultrahigh carbon steel under a range of heat treatments. Applying image representations from contemporary computer vision research to these microstructures, we discuss how both supervised and unsupervised machine learning techniques can be used to yield insight into microstructural trends and their relationship to processing conditions. We evaluate and compare keypoint-based and convolutional neural network representations by classifying microstructures according to their primary microconstituent, and by classifying a subset of the microstructures according to the annealing conditions that generated them. Using t-SNE, a nonlinear dimensionality reduction and visualization technique, we demonstrate graphical methods of exploring microstructure and processing datasets, and for understanding and interpreting high-dimensional microstructure representations.

I s this what we've got people thinking? I've never been much of a DMX fan but it hurts my heart to see someone spit this type of shit about the culture we've embraced growing up out here in these Los Angeles streets. A lot of boys became men out here from Inglewood all the way out to the Inland Empire and back. What about the Watts Riots in the 60s and the repeat in the early 90s that engulfed most of L.A. in flames? What about the countless acts of racially motivated police brutality that go unnoticed every day?

Given the heated debate surrounding art in public places, the conflicting claims about what people think and feel about such works, the purpose of this study was to build on previous research involving art, public, and place to explore the social and psychological experience of art in the environment. In Part I, the Comparative Site Study, four murals in the Newcastle Upon Tyne region of Northern England were studied. The individual mural settings selected were either residential or commercial locales. The artworks, themselves, differed in terms of how they were created, involving either community participation or no community participation. Interviewing and observing were carried out at all four sites and data were obtained that could be analyzed on a comparative basis. Part II of this research was a case study that included an in-depth analysis of the impact of the creation of a participatory art work, a mural in a residential place. Work on this mural included the researcher's own involvement as facilitator of this participatory project. The results of this study indicated that almost a third of those passing by the murals reported being aware of them. Although all of the murals were positively rated, those that did not involve any community participation received the highest ratings. This was particularly true where the work was done by a local artist, depicting local scenes with personal meaning. Some participant's sense of their creativity was increased through involvement with the mural project. Despite these findings, people had differing feelings about whether or not murals had a positive impact on places. People's opinions of the murals did not affect their opinions of either the setting or neighborhood containing the artwork. Passerby's experience of the murals did not measurably differ in residential versus commercial locales. Participation in a mural project did not increase participants' or residents' sense of belonging, community, or commitment to place. Overall, however, results indicated that the overwhelming majority of those interviewed were in favor of more murals in the public domain.