882 research outputs found
Sounding Out the Reading Debate: The Efficacy of Explicit Phonics Instruction Within a Whole Language Reading Curriculum
Controversy over how to teach reading centers around phonics and whole language and whether phonics should be taught in isolation. Previous studies have compared the two methods rather than combinations of both, and have utilized standardized tests that have questionable usefulness. This study proposed that curriculum based measurement is a more accurate measurement. Reading probes were administered to 38 students in nongraded classrooms. Both classrooms incorporated phonics into whole language curriculums; however, only one classroom used the Spalding method of phonics instruction. A pretest, posttest design was utilized, and gain scores were compared using a t-test. Results indicated a significant difference in fluency gain. The hypothesis that the classroom integrating the Spalding method would exhibit greater fluency gain was supported
Nebraska Farm Building Data for North-Central Counties taken from U.S. Census
The Material given on the following pages was selected from United States Census data for the years indicated.
It has been arranged to permit analysis and comparison of building trends since 1900, both in the state and in individual counties. Such a study often reveals areas in which effective educational programs could be developed and indicates the phases of such programs which are needed most.
Unfortunately, complete 1945 figures are not available yet, but space has been left for them so that they may be added when released by the Census Bureau
CLOTH3D: Clothed 3D Humans
This work presents CLOTH3D, the first big scale synthetic dataset of 3D
clothed human sequences. CLOTH3D contains a large variability on garment type,
topology, shape, size, tightness and fabric. Clothes are simulated on top of
thousands of different pose sequences and body shapes, generating realistic
cloth dynamics. We provide the dataset with a generative model for cloth
generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on
graph convolutions (GCVAE) to learn garment latent spaces. This allows for
realistic generation of 3D garments on top of SMPL model for any pose and
shape
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
The Ekman-Hartmann layer in MHD Taylor-Couette flow
We study magnetic effects induced by rigidly rotating plates enclosing a
cylindrical MHD Taylor-Couette flow at the finite aspect ratio . The
fluid confined between the cylinders is assumed to be liquid metal
characterized by small magnetic Prandtl number, the cylinders are perfectly
conducting, an axial magnetic field is imposed \Ha \approx 10, the rotation
rates correspond to \Rey of order . We show that the end-plates
introduce, besides the well known Ekman circulation, similar magnetic effects
which arise for infinite, rotating plates, horizontally unbounded by any walls.
In particular there exists the Hartmann current which penetrates the fluid,
turns into the radial direction and together with the applied magnetic field
gives rise to a force. Consequently the flow can be compared with a Taylor-Dean
flow driven by an azimuthal pressure gradient. We analyze stability of such
flows and show that the currents induced by the plates can give rise to
instability for the considered parameters. When designing an MHD Taylor-Couette
experiment, a special care must be taken concerning the vertical magnetic
boundaries so they do not significantly alter the rotational profile.Comment: 9 pages, 6 figures; accepted to PR
Real-time gestural control of robot manipulator through Deep Learning human-pose inference
International audienceWith the raise of collaborative robots, human-robot interaction needs to be as natural as possible. In this work, we present a framework for real-time continuous motion control of a real collabora-tive robot (cobot) from gestures captured by an RGB camera. Through deep learning existing techniques, we obtain human skeletal pose information both in 2D and 3D. We use it to design a controller that makes the robot mirror in real-time the movements of a human arm or hand
Inner Space Preserving Generative Pose Machine
Image-based generative methods, such as generative adversarial networks
(GANs) have already been able to generate realistic images with much context
control, specially when they are conditioned. However, most successful
frameworks share a common procedure which performs an image-to-image
translation with pose of figures in the image untouched. When the objective is
reposing a figure in an image while preserving the rest of the image, the
state-of-the-art mainly assumes a single rigid body with simple background and
limited pose shift, which can hardly be extended to the images under normal
settings. In this paper, we introduce an image "inner space" preserving model
that assigns an interpretable low-dimensional pose descriptor (LDPD) to an
articulated figure in the image. Figure reposing is then generated by passing
the LDPD and the original image through multi-stage augmented hourglass
networks in a conditional GAN structure, called inner space preserving
generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human
figures, which are highly articulated with versatile variations. Test of a
state-of-the-art pose estimator on our reposed dataset gave an accuracy over
80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to
preserve the background with high accuracy while reasonably recovering the area
blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
We present a method for simultaneously estimating 3D human pose and body
shape from a sparse set of wide-baseline camera views. We train a symmetric
convolutional autoencoder with a dual loss that enforces learning of a latent
representation that encodes skeletal joint positions, and at the same time
learns a deep representation of volumetric body shape. We harness the latter to
up-scale input volumetric data by a factor of , whilst recovering a
3D estimate of joint positions with equal or greater accuracy than the state of
the art. Inference runs in real-time (25 fps) and has the potential for passive
human behaviour monitoring where there is a requirement for high fidelity
estimation of human body shape and pose
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