1,422 research outputs found
bíogo/hts: high throughput sequence handling for the Go language
biogo/hts provides a Go native implementation of the SAM specification (Group 2016) for SAM and BAM alignment formats (H. et al. 2012) commonly used for representation of high throughput genomic data, the BAI, CSI and tabix indexing formats, and the BGZF blocked compression format. The biogo/hts packages perform parallelized read and write operations and are able to cache recent reads according to user-specified caching methods. The parallelisation approach used by the biogo/hts package is influenced by the approach of the D implementation, sambamba by Tarazov et al. (T. A. et al. 2015). The biogo/hts APIs have been constructed to provide a consistent interface to sequence alignment data and the underlying compression system in order to aid ease of use and tool development.R. Daniel Kortschak, Brent S. Pedersen, and David L. Adelso
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Exploring the Limitations of Measures of Students` Socioeconomic Status (SES)
This study uses a nationally representative student dataset to explore the limitations of commonly used measures of socioeconomic status (SES). Among the identified limitations are patterns of missing data that conflate the traditional conceptualization of SES with differences in family structure that have emerged in recent years and a lack of theoretically-based guidance for how the components of SES should be combined. Using kindergarten achievement data, the study illustrates how both the observed relation between SES and achievement and the observed interaction between SES and kindergarten program would be impacted by the use of different measures of SES. This study also explores the measurement of SES within a structural equation modeling (SEM) framework, highlighting both the relevant conceptual and measurement issues. Accessed 9,241 times on https://pareonline.net from May 15, 2014 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right
Playing with Puffball: Simple Scale-Invariant Inflation for Use in Vision and Graphics
We describe how inflation, the act of mapping a 2D silhouette to a 3D region, can be applied in two disparate problems to offer insight and improvement: silhouette part segmentation and image-based material transfer. To demonstrate this, we introduce Puffball, a novel inflation technique, which achieves similar results to existing inflation approaches -- including smoothness, robustness, and scale and shift-invariance -- through an exceedingly simple and accessible formulation. The part segmentation algorithm avoids many of the pitfalls of previous approaches by finding part boundaries on a canonical 3-D shape rather than in the contour of the 2-D shape; the algorithm gives reliable and intuitive boundaries, even in cases where traditional approaches based on the 2D Minima Rule are misled. To demonstrate its effectiveness, we present data in which subjects prefer Puffball's segmentations to more traditional Minima Rule-based segmentations across several categories of silhouettes. The texture transfer algorithm utilizes Puffball's estimated shape information to produce visually pleasing and realistically synthesized surface textures with no explicit knowledge of either underlying shape.National Eye Institute (Special Training Grant
ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition
Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data
Molecular Line Profile Fitting with Analytic Radiative Transfer Models
We present a study of analytic models of starless cores whose line profiles
have ``infall asymmetry,'' or blue-skewed shapes indicative of contracting
motions. We compare the ability of two types of analytical radiative transfer
models to reproduce the line profiles and infall speeds of centrally condensed
starless cores whose infall speeds are spatially constant and range between 0
and 0.2 km s-1. The model line profiles of HCO+ (J=1-0) and HCO+ (J=3-2) are
produced by a self-consistent Monte Carlo radiative transfer code. The analytic
models assume that the excitation temperature in the front of the cloud is
either constant (``two-layer'' model) or increases inward as a linear function
of optical depth (``hill'' model). Each analytic model is matched to the line
profile by rapid least-squares fitting.
The blue-asymmetric line profiles with two peaks, or with a blue shifted peak
and a red shifted shoulder, can be well fit by the ``HILL5'' model (a five
parameter version of the hill model), with an RMS error of 0.02 km s-1. A peak
signal to noise ratio of at least 30 in the molecular line observations is
required for performing these analytic radiative transfer fits to the line
profiles.Comment: 48 pages, 20 figures, accepted for publication in Ap
Millimeter and Submillimeter Survey of the R Corona Australis Region
Using a combination of data from the Antarctic Submillimeter Telescope and
Remote Observatory (AST/RO), the Arizona Radio Observatory Kitt Peak 12m
telescope and the Arizona Radio Observatory 10m Heinrich Hertz Telescope, we
have studied the most active part of the R CrA molecular cloud in multiple
transitions of Carbon Monoxide, HCO and 870\micron continuum emission.
Since R CrA is nearby (130 pc), we are able to obtain physical spatial
resolution as high as 0.01pc over an area of 0.16 pc, with velocity
resolution finer than 1 km/s. Mass estimates of the protostar driving the
mm-wave emission derived from HCO, dust continuum emission and kinematic
techniques point to a young, deeply embedded protostar of 0.5-0.75
M, with a gaseous envelope of similar mass. A molecular outflow is
driven by this source that also contains at least 0.8 M of molecular
gas with 0.5 L of mechanical luminosity. HCO lines show the
kinematic signature of infall motions as well as bulk rotation. The source is
most likely a Class 0 protostellar object not yet visible at near-IR
wavelengths. With the combination of spatial and spectral resolution in our
data set, we are able to disentangle the effects of infall, rotation and
outflow towards this young object.Comment: 29 pages, 9 figures. Accepted for publication in the Astrophysical
Journa
Dichotomy in the Dynamical Status of Massive Cores in Orion
To study the evolution of high mass cores, we have searched for evidence of
collapse motions in a large sample of starless cores in the Orion molecular
cloud. We used the Caltech Submillimeter Observatory telescope to obtain
spectra of the optically thin (\H13CO+) and optically thick (\HCO+) high
density tracer molecules in 27 cores with masses 1 \Ms. The red- and
blue-asymmetries seen in the line profiles of the optically thick line with
respect to the optically thin line indicate that 2/3 of these cores are not
static. We detect evidence for infall (inward motions) in 9 cores and outward
motions for 10 cores, suggesting a dichotomy in the kinematic state of the
non-static cores in this sample. Our results provide an important observational
constraint on the fraction of collapsing (inward motions) versus non-collapsing
(re-expanding) cores for comparison with model simulations.Comment: 9 pages, 2 Figures. To appear in ApJ(Letters
Tracking objects with point clouds from vision and touch
We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified second-order update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot's end effector
Infall, Outflow, Rotation, and Turbulent Motions of Dense Gas within NGC 1333 IRAS 4
Millimeter wavelength observations are presented of NGC 1333 IRAS 4, a group
of highly-embedded young stellar objects in Perseus, that reveal motions of
infall, outflow, rotation, and turbulence in the dense gas around its two
brightest continuum objects, 4A and 4B. These data have finest angular
resolution of approximately 2" (0.0034 pc) and finest velocity resolution of
0.13 km/s. Infall motions are seen from inverse P-Cygni profiles observed in
H2CO 3_12-2_11 toward both objects, but also in CS 3-2 and N2H+ 1-0 toward 4A,
providing the least ambiguous evidence for such motions toward low-mass
protostellar objects. Outflow motions are probed by bright line wings of H2CO
3_12-2_11 and CS 3-2 observed at positions offset from 4A and 4B, likely
tracing dense cavity walls. Rotational motions of dense gas are traced by a
systematic variation of the N2H+ line velocities, and such variations are found
around 4A but not around 4B. Turbulent motions appear reduced with scale, given
N2H+ line widths around both 4A and 4B that are narrower by factors of 2 or 3
than those seen from single-dish observations. Minimum observed line widths of
approximately 0.2 km/s provide a new low, upper bound to the velocity
dispersion of the parent core to IRAS 4, and demonstrate that turbulence within
regions of clustered star formation can be reduced significantly. A third
continuum object in the region, 4B', shows no detectable line emission in any
of the observed molecular species.Comment: LateX, 51 pages, 9 figures, accepted by Ap
Deep Depth From Focus
Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201
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