A multi-scale, multi-wavelength source extraction method: getsources

We present a multi-scale, multi-wavelength source extraction algorithm called getsources. Although it has been designed primarily for use in the far-infrared surveys of Galactic star-forming regions with Herschel, the method can be applied to many other astronomical images. Instead of the traditional approach of extracting sources in the observed images, the new method analyzes fine spatial decompositions of original images across a wide range of scales and across all wavebands. It cleans those single-scale images of noise and background, and constructs wavelength-independent single-scale detection images that preserve information in both spatial and wavelength dimensions. Sources are detected in the combined detection images by following the evolution of their segmentation masks across all spatial scales. Measurements of the source properties are done in the original background-subtracted images at each wavelength; the background is estimated by interpolation under the source footprints and overlapping sources are deblended in an iterative procedure. In addition to the main catalog of sources, various catalogs and images are produced that aid scientific exploitation of the extraction results. We illustrate the performance of getsources on Herschel images by extracting sources in sub-fields of the Aquila and Rosette star-forming regions. The source extraction code and validation images with a reference extraction catalog are freely available.Comment: 31 pages, 27 figures, to be published in Astronomy & Astrophysic

Simulated Adversarial Testing of Face Recognition Models

Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be revealed at test time in the real world. The risks involved in such failures can be loss of profits, loss of time or even loss of life in certain critical applications. In order to alleviate this issue, simulators can be controlled in a fine-grained manner using interpretable parameters to explore the semantic image manifold. In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios. We apply this model in a face recognition scenario. We are the first to show that weaknesses of models trained on real data can be discovered using simulated samples. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected components in the latent space of the simulator. We present a method to find these adversarial regions as opposed to the typical adversarial points found in the adversarial example literature

APRIL: Approximating Polygons as Raster Interval Lists

The spatial intersection join an important spatial query operation, due to its popularity and high complexity. The spatial join pipeline takes as input two collections of spatial objects (e.g., polygons). In the filter step, pairs of object MBRs that intersect are identified and passed to the refinement step for verification of the join predicate on the exact object geometries. The bottleneck of spatial join evaluation is in the refinement step. We introduce APRIL, a powerful intermediate step in the pipeline, which is based on raster interval approximations of object geometries. Our technique applies a sequence of interval joins on 'intervalized' object approximations to determine whether the objects intersect or not. Compared to previous work, APRIL approximations are simpler, occupy much less space, and achieve similar pruning effectiveness at a much higher speed. Besides intersection joins between polygons, APRIL can directly be applied and has high effectiveness for polygonal range queries, within joins, and polygon-linestring joins. By applying a lightweight compression technique, APRIL approximations may occupy even less space than object MBRs. Furthermore, APRIL can be customized to apply on partitioned data and on polygons of varying sizes, rasterized at different granularities. Our last contribution is a novel algorithm that computes the APRIL approximation of a polygon without having to rasterize it in full, which is orders of magnitude faster than the computation of other raster approximations. Experiments on real data demonstrate the effectiveness and efficiency of APRIL; compared to the state-of-the-art intermediate filter, APRIL occupies 2x-8x less space, is 3.5x-8.5x more time-efficient, and reduces the end-to-end join cost up to 3 times.Comment: 12 page

Damage Process Assessment of Mortar Samples under Freeze-thaw Cycleswith Micro-CT and Expansion Measurements

The aim of this study is to comprehensively investigate the relationship between 3- dimensional crack development and mechanical degradation during Freeze-thaw cycles (FTC). An FTC test was designed in this study to relate the micro-scale crack patterns to the macro-scale expansion and mechanical property deterioration at different FTC damage levels. Mortar specimens with waterto-cement (w/c) ratios of 50% and 75% were cast in two sizes (i.e., Ø5×10 cm, Ø2×2.5 cm) and were subjected to FTCs. For Ø5×10 cm specimens, strain in the center part of the specimens were monitored by embedded mold gauges and compression tests were conducted at different expansion levels. For Ø2×2.5 cm specimens, X-ray micro computed tomography (micro-CT) and compression tests were conducted after different FTC durations. By comparing the test results of these two groups of specimens, the expansion, mechanical degradation, and development of micro-cracks in the mortar specimens during the FTC damage process were correlated. It is indicated that with similar mechanical reduction, the damage pattern differs in two w/c cases. This research provides a test method for investigating internal swelling damage and proposes the direction for further improvement of FTC simulation model

Deming Headlight, 08-04-1894

https://digitalrepository.unm.edu/deming_headlight_news/1158/thumbnail.jp

Western Liberal, 05-24-1889

https://digitalrepository.unm.edu/lwl_news/1117/thumbnail.jp

Carrizozo Outlook, 08-19-1921

https://digitalrepository.unm.edu/c_outlook_news/1286/thumbnail.jp

Systematic mask synthesis for surface micromachined microelectromechanical systems

In the context of designing surface-micromachined microelectromechanical systems (MEMS), there does not appear to be systematic means, with the exception of parametrized layout models, to generate the mask data after the geometric model of a MEMS device is refined through behavioral simulations. This paper focuses on automatically generating masks, given a geometric model of the MEMS device and the process sequence (referred to here as the inverse problem). This necessitates a systematic solution of the forward problem, which involves automatically generating a geometric model of the MEMS device given the masks. A systematic and implementation-independent framework for the geometric modeling of MEMS is presented in order to solve the forward and inverse problems for general surface-micromachined devices. In particular, the geometric problem of mask synthesis is reduced to a system of linear equations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49041/2/jm3616.pd