We present a flexible linear optimization model for correcting multi-angle curtaining effects in plasma focused ion beam scanning electron microscopy (PFIB-SEM) images produced by rocking-polishing schemes. When PFIB-SEM is employed in a serial sectioning tomography workow, it is capable of imaging large three-dimensional volumes quickly, providing rich information in the critical 10–100 nm feature length scale. During tomogram acquisition, a “rocking polish” is often used to reduce straight-line “curtaining” gradations in the milled sample surface. While this mitigation scheme is effective for deep curtains, it leaves shallower line artifacts at two discretized angles. Segmentation and other automated processing of the image set requires that these artifacts be corrected for accurate microstructural quantification. Our work details a new Fourier-based linear optimization model for correcting curtaining artifacts by targeting curtains at two discrete angles. We demonstrate its capabilities by processing images from a tomogram from a multiphase, heterogeneous concrete sample. We present methods for selecting the parameters which meet the user’s goals most appropriately. Compared to previous works, we show that our model provides effective multi-angle curtain correction without introducing artifacts into the image, modifying non-curtain structures or causing changes to the contrast of voids. Our algorithm can be easily parallelized to take advantage of multi-core hardware.

Focused ion beam scanning electron microscope tomography is a destructive slice-and-image technique for obtaining three-dimensional structural information. Xenon plasma focused ion beam (PFIB) is a promising new ion source technology that has a much higher material removal rate than traditional gallium source technology -greatly increasing the analyzable volume of material [1].Unfortunately, due to the heterogeneous nature of any interesting sample, plasma milling rates vary, causing vertical ripples ("curtains") on the imaged cut face of the sample. These curtains appear as dark or light straight-line artifacts on secondary and backscatter electron images. In order to perform an accurate automated quantitative analysis of the resulting tomogram, these artifacts must be reduced as much as possible through physical means, such as using a deposited capping layer on the top of the sample or through a "rocking polish" technique which works by milling the sample at two discrete angles between image acquisitions. While these techniques help to reduce the ef-fect, curtains remain at two angles and need to be further reduced through image processing means.We have developed a new algorithm to effectively correct these image artifacts by applying a Fourier-based linear optimization model to detect and remove curtains at two discretized angles. The algorithm and linear optimization model was implemented in the Anaconda Python 3.5.2 distribution using the open-source Gnu Linear Programming Kit (GLPK) to solve the linear optimization problem on small image blocks, then stitch the corrected image back together.In order to test the algorithm, we ran the algorithm on a ultra-high performance concrete tomogram taken on an FEI PFIB. A backscatter electron detector was used to take images at 48nm × 48nm pixel resolution with a 50nm slice thickness. During imaging, a rocking mill was used, leaving curtains at approximately 1 • and -7 • from the vertical. We have shown that our algorithm is able to effectively detect and correct curtaining artifacts without causing changes to other structures (see figures 1 & 2).Future work includes taking advantage of the parallel nature of the algorithm and using faster implementations and solvers in order to speed up the processing. Additionally, by combining multispectral data, such as information from both backscatter and secondary electron images, we believe we can obtain more effective curtain detection and correction. Other works meant to reduce only vertical curtains (such as [2]) take advantage of three-dimensional inter-slice information, which may prove advantageous to our model and should be further explored [3].

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In recent years, there has been increasing interest in developing a Computer Science curriculum for K-8 students. However, there have been significant barriers to creating and deploying a Computer Science curriculum in many areas, including teacher time and the prioritization of other 21st-century skills. At McMaster University, we have developed both general computer literacy activities and specific programming activities. Integration of these activities is made easy as they each support existing curricular goals. In this paper, we focus on programming in the functional language Elm and the graphics library GraphicSVG. Elm is in the ML (Meta Language) family, with a lean syntax and easy inclusion of Domain Specific Languages. This allows children to start experimenting with GraphicSVG as a language for describing shape, and pick up the core Elm language as they grow in sophistication. Teachers see children making connections between computer graphics and mathematics within the first hour. Graphics are defined declaratively, and support aggregation and transformation, i.e., Algebra. Variables are not needed initially, but are introduced as a time-saving feature, which is immediately accepted. Since variables are declarative, they match students' expectations. Advanced students are also exposed to State by making programs that react to user taps or clicks. The syntax required to do so closely follows the theoretical concepts, making it easy for them to grasp. For each of these concepts, we explain how they fit into the presentations we make to students, like the 5200 children taught in 2016.Finally, we describe ongoing work on a touch-based Elm editor for iPad, which features (1) type highlighting (as opposed to syntax highlighting), (2) preservation of correct syntax and typing across transformations, (3) context information (e.g. displaying parameter names for GraphicSVG functions), and (4) immediate feedback (e.g. restarting animations after every program change).

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