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 $H/D=10$. 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 $10^2-10^3$. 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 $4 \times$, 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