Search for: All records

We present new observations of 16 bright (r = 19–21) gravitationally lensed galaxies at z ≃ 1–3 selected from the CASSOWARY survey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogues at high redshift (g = 20.5), with a large specific star formation rate (31.2 Gyr−1) and an [O iii]+H β equivalent width (EW[O iii] + H β = 730 Å) that is nearly identical to the average value expected at z ≃ 7–8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong C iii] emission. Although most of the sources in our sample show weak UV line emission, we find elevated C iii] in the spectrum of CSWA-141 (EWC iii] = 4.6 ± 1.9 Å) together with detections of other prominent emission lines (O iii], Si iii], Fe ii⋆, Mg ii). We compare the rest-optical line properties of high-redshift galaxies with strong and weak C iii] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigationmore »of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the C iii] doublet likely probes higher densities than that traced by [O ii] and [S ii]. Characterization of the spectrally resolved Mg ii emission line and several low-ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.

« less

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult caribou”, “calf caribou”, and “ghost caribou” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99,Pvalue < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.

Abstract The embedded finite element technique provides a unique approach for modeling of fiber-reinforced composites. Meshing fibers as distinct bundles represented by truss elements embedded in a matrix material mesh allows for the assignment of more specific material properties for each component rather than homogenization of all of the properties. However, the implementations of the embedded element technique available in commercial software do not replace the material of the matrix elements with the material of the embedded elements. This causes a redundancy in the volume calculation of the overlapping meshes leading to artificially increased stiffness and mass. This paper investigates the consequences in the energy calculations of an explicit dynamic model due to this redundancy. A method for the correction of the edundancy within a finite element code is suggested which removes extra energy and is shown to be effective at correcting the energy calculations for large amounts of redundant volume.

Abstract Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains.