Lipschitz-Volume rigidity on limit spaces with Ricci curvature bounded from below

We prove a Lipschitz-Volume rigidity theorem for the non-collapsed Gromov-Hausdorff limits of manifolds with Ricci curvature bounded from below. This is a counterpart of the Lipschitz-Volume rigidity in Alexandrov geometry

Cosmology-Independent Distance Moduli of 42 Gamma-Ray Bursts between Redshift of 1.44 and 6.60

This report is an update and extension of our paper accepted for publication in ApJ (arXiv:0802.4262). Since objects at the same redshift should have the same luminosity distance and the distance moduli of type Ia supernovae (SNe Ia) obtained directly from observations are completely cosmology independent, we obtain the distance modulus of a gamma-ray burst (GRB) at a given redshift by interpolating or iterating from the Hubble diagram of SNe Ia. Then we calibrate five GRB relations without assuming a particular cosmological model, from different regression methods, and construct the GRB Hubble diagram to constrain cosmological parameters. Based upon these relations we list the cosmology-independent distance moduli of 42 GRBs between redshift of 1.44 and 6.60, with the 1-$\sigma$ uncertainties of 1-3%.Comment: 6 pages, 2 figures, 3 tables. To appear in the proceedings of "2008 Nanjing GRB conference", Nanjing, 23-27 June 200

Applying Multiple Streams Theoretical Framework to College Matriculation Policy Reform for Children of Migrant Workers in China

Pre-decision, as the first step in the process of public policy-making, includes agenda setting and alternative specification. In order to make a better understanding of Chinese pre-decision processes and explore Chinese special characteristics presented in the processes of pre-decision, the article researches the case of college matriculation policy reform for children of migrant workers by applying multiple streams theoretical framework in the Chinese context. It analyzes how the political stream can move this policy reform problem up on the governmental agenda directly and points out that it always fails to enter the decision agenda due to the absence of the policy stream. The article also argues that a surviving proposal in our case not only need to satisfy necessary criteria, but to consider obstructions of Chinese particular institutions. Finally, the article concludes that the multiple streams theory is generally applicable in China and proves the significance that the policy stream has been ready to wait for the link of other two streams. Through this case research, we can provide theoretical supports and practical experiences for Chinese governmental officials in the processes of their pre-decision and make them optimize the processes well in future. Keywords: multiple streams, policy window, college matriculation policy reform (CMPR), agenda setting, alternative

Monotonicity Results for Arithmetic Means of Concave and Convex Functions

By majorization approaches, some known results on monotonicity of the arithmetic means of convex and concave functions are proved and generalized once again

Learning to predict under a budget

Prediction-time budgets in machine learning applications can arise due to monetary or computational costs associated with acquiring information; they also arise due to latency and power consumption costs in evaluating increasingly more complex models. The goal in such budgeted prediction problems is to learn decision systems that maintain high prediction accuracy while meeting average cost constraints during prediction-time. Such decision systems can potentially adapt to the input examples, predicting most of them at low cost while allocating more budget for the few "hard" examples. In this thesis, I will present several learning methods to better trade-off cost and error during prediction. The conceptual contribution of this thesis is to develop a new paradigm of bottom-up approach instead of the traditional top-down approach. A top-down approach attempts to build out the model by selectively adding the most cost-effective features to improve accuracy. In contrast, a bottom-up approach first learns a highly accurate model and then prunes or adaptively approximates it to trade-off cost and error. Training top-down models in case of feature acquisition costs leads to fundamental combinatorial issues in multi-stage search over all feature subsets. In contrast, we show that the bottom-up methods bypass many of such issues. To develop this theme, we first propose two top-down methods and then two bottom-up methods. The first top-down method uses margin information from training data in the partial feature neighborhood of a test point to either select the next best feature in a greedy fashion or to stop and make prediction. The second top-down method is a variant of random forest (RF) algorithm. We grow decision trees with low acquisition cost and high strength based on greedy mini-max cost-weighted impurity splits. Theoretically, we establish near-optimal acquisition cost guarantees for our algorithm. The first bottom-up method we propose is based on pruning RFs to optimize expected feature cost and accuracy. Given a RF as input, we pose pruning as a novel 0-1 integer program and show that it can be solved exactly via LP relaxation. We further develop a fast primal-dual algorithm that scales to large datasets. The second bottom-up method is adaptive approximation, which significantly generalizes the RF pruning to accommodate more models and other types of costs besides feature acquisition cost. We first train a high-accuracy, high-cost model. We then jointly learn a low-cost gating function together with a low-cost prediction model to adaptively approximate the high-cost model. The gating function identifies the regions of the input space where the low-cost model suffices for making highly accurate predictions. We demonstrate empirical performance of these methods and compare them to the state-of-the-arts. Finally, we study adaptive approximation in the on-line setting to obtain regret guarantees and discuss future work.2019-07-02T00:00:00