Inequality and Education Choice

* Revised： [16-17, 2016

Human Capital, Public Debt, and Economic Growth : A Political Economy Analysis

This study considers the politics of public education policy in an overlapping-generations model with physical and human capital accumulation. In particular, this study examines how debt and tax financing differ in terms of growth and welfare across generations, as well as which fiscal stance voters support. The analysis shows that the growth rate in debt financing is lower than that in tax financing, and that debt financing creates a tradeoff between the present and future generations. The analysis also shows that debt financing attains slower economic growth than that realized by the choice of a social planner who cares about the welfare of all generations.* Revised： [16-01, 2016]* Revised： [16-01-Rev., 2016]* Revised： [16-01-Rev.2, 2017

Pensions, Education, and Growth : A Positive Analysis

* Revised： [14-37, 2014]* Revised： [14-37-Rev., 2015

Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light

One of the solutions of depth imaging of moving scene is to project a static pattern on the object and use just a single image for reconstruction. However, if the motion of the object is too fast with respect to the exposure time of the image sensor, patterns on the captured image are blurred and reconstruction fails. In this paper, we impose multiple projection patterns into each single captured image to realize temporal super resolution of the depth image sequences. With our method, multiple patterns are projected onto the object with higher fps than possible with a camera. In this case, the observed pattern varies depending on the depth and motion of the object, so we can extract temporal information of the scene from each single image. The decoding process is realized using a learning-based approach where no geometric calibration is needed. Experiments confirm the effectiveness of our method where sequential shapes are reconstructed from a single image. Both quantitative evaluations and comparisons with recent techniques were also conducted.Comment: 9 pages, Published at the International Conference on Computer Vision (ICCV 2017

Quantum Phase Transition in Lattice Model of Unconventional Superconductors

In this paper we shall introduce a lattice model of unconventional superconductors (SC) like d-wave SC in order to study quantum phase transition at vanishing temperature ($T$). Finite-$T$ counterpart of the present model was proposed previously with which SC phase transition at finite $T$ was investigated. The present model is a noncompact U(1) lattice-gauge-Higgs model in which the Higgs boson, the Cooper-pair field, is put on lattice links in order to describe d-wave SC. We first derive the model from a microscopic Hamiltonian in the path-integral formalism and then study its phase structure by means of the Monte Carlo simulations. We calculate the specific heat, monopole densities and the magnetic penetration depth (the gauge-boson mass). We verified that the model exhibits a second-order phase transition from normal to SC phases. Behavior of the magnetic penetration depth is compared with that obtained in the previous analytical calculation using XY model in four dimensions. Besides the normal to SC phase transition, we also found that another second-order phase transition takes place within the SC phase in the present model. We discuss physical meaning of that phase transition.Comment: 12 pages, 10 figures, references added, some discussion on the results adde

Learning to Find Good Correspondences

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while imbuing it with global information, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data.Comment: CVPR 2018 (Oral

Growth Plate Borderline Chondrocytes Behave as Transient Mesenchymal Precursor Cells

The growth plate provides a substantial source of mesenchymal cells in the endosteal marrow space during endochondral ossification. The current model postulates that a group of chondrocytes in the hypertrophic zone can escape from apoptosis and transform into cells that eventually become osteoblasts in an area beneath the growth plate. The growth plate is composed of cells with various morphologies; particularly at the periphery of the growth plate immediately adjacent to the perichondrium are “borderline” chondrocytes, which align perpendicularly to other chondrocytes. However, in vivo cell fates of these special chondrocytes have not been revealed. Here we show that borderline chondrocytes in growth plates behave as transient mesenchymal precursor cells for osteoblasts and marrow stromal cells. A single‐cell RNA‐seq analysis revealed subpopulations of Col2a1‐creER‐marked neonatal chondrocytes and their cell type–specific markers. A tamoxifen pulse to Pthrp‐creER mice in the neonatal stage (before the resting zone was formed) preferentially marked borderline chondrocytes. Following the chase, these cells marched into the nascent marrow space, expanded in the metaphyseal marrow, and became Col(2.3 kb)‐GFP+ osteoblasts and Cxcl12‐GFPhigh reticular stromal “CAR” cells. Interestingly, these borderline chondrocyte‐derived marrow cells were short‐lived, as they were significantly reduced during adulthood. These findings demonstrate based on in vivo lineage‐tracing experiments that borderline chondrocytes in the peripheral growth plate are a particularly important route for producing osteoblasts and marrow stromal cells in growing murine endochondral bones. A special microenvironment neighboring the osteogenic perichondrium might endow these chondrocytes with an enhanced potential to differentiate into marrow mesenchymal cells. © 2019 American Society for Bone and Mineral Research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151266/1/jbmr3719_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151266/2/jbmr3719-sup-0001-Suppl_Info_JBMR_021819.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151266/3/jbmr3719.pd

Robust Constrained Hyperspectral Unmixing Using Reconstructed-Image Regularization

Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between sparse and piecewise-smooth regularizations for abundance maps is carefully adjusted, noise may remain in the estimated abundance maps or undesirable artifacts may appear. Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image. To overcome these limitations, we propose a new robust hyperspectral unmixing method based on constrained convex optimization. Our method employs, in addition to the two regularizations for the abundance maps, regularizations for the HS image reconstructed by mixing the estimated abundance maps and endmembers. This strategy makes the unmixing process much more robust in highly-noisy scenarios, under the assumption that the abundance maps used to reconstruct the HS image with desirable spatio-spectral structure are also expected to have desirable properties. Furthermore, our method is designed to accommodate a wider variety of noise including stripe noise. To solve the formulated optimization problem, we develop an efficient algorithm based on a preconditioned primal-dual splitting method, which can automatically determine appropriate stepsizes based on the problem structure. Experiments on synthetic and real HS images demonstrate the advantages of our method over existing methods.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensin