A robust automatic clustering algorithm for probability density functions with application to categorizing color images

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

高精度低速数据采集系统

本文为Applel Ⅱ微型计算机提供了一种廉价的高精度低速数据采集系统。该系统的采样频率为3Hz,精度为1/20000,并拥有8个模拟输入通道,适用于仪表自动记录等低速环境。     

The disaster relief and social work after the Wenchuan earthquake in Mainland China

ABSTRACT

This paper mainly focuses on the development of disaster social work in Mainland China and the intervention of social work in disaster relief. Before the Wenchuan earthquake and in the initial stage of post-earthquake, disaster social work was mainly based on individual psychotherapy; from the earthquake to the year of 2012 in which post-earthquake recovery and reconstruction was completed, the disaster relief began to emphasise community building and integration, while the basic framework for disaster social work was also established. Social workers begin to explore the new mode of developmental and localised disaster social work. By combining with the practical experiences from disaster social work, this paper tries to highlight the dilemmas confronting disaster relief in Mainland China and put forward some corresponding countermeasures and suggestions, which could improve the future disaster relief system in Mainland China.     

德国科技创新体系的治理特征及实践启示

二战以后,德国在世界经济版图中一直占据重要位置,表现得稳健而强劲.有效的科技创新体系是德国经济社会可持续发展的关键成功因素.基于德国科技创新体系结构与功能的分析,从创新基础、治理结构、制度环境、国际科技合作、文化传统与社会环境等方面对德国科技创新体系的治理特征进行梳理和归纳,结合上海创新驱动发展的实践需求,研究德国科技创新体系对于上海创新发展的意义.     

央行沟通对于货币政策实际干预的预测能力研究

信息沟通有助于公众理解和预测货币政策的实际干预,进而提高货币政策有效性。研究中国人民银行的信息沟通对实际政策干预的预测能力需要解决沟通文本的测度问题,同时考虑实际政策干预多工具并用的复杂性。基于此,本文通过文本分析方法提取《货币政策执行报告》的信息,进而考察央行沟通对于货币政策实际干预的预测能力。研究表明,整体而言,市场对中国人民银行“听其言”有助于“观其行”。具体来看,央行沟通对于直接可控的基准利率和存款准备金率具有持续的预测能力,但对于市场利率的预测能力较差,对于M2增长率的预测甚至存在方向不一致的情况。     

适于DSP的模拟信号调制方式自动识别方法研究

提出一种适用于DSP的基于Bessel插值结构的正交数字下变频方案，可实现已调信号调制方式的自动识别。该方案能够实现提取信号的正交分量和同相分量，并且能够提取出较高精度的信号的基本参数，由此得到的特征参数可以准确地识别出信号的调制模式。最后，通过计算机和DSP仿真，证明了它的可行性。     

THE IMPORTANCE OF LEGAL FORM OF ORGANIZATION ON SMALL CORPORATION EXTERNAL FINANCING

This article quantitatively evaluates the impact of legal form of organization (LFO) choices, C versus S corporation, on small business external financing. A treatment effect model is formulated and estimated to examine the relationship between corporate types and chances of obtaining external financing. The estimation takes into account self‐selection bias associated with LFO choices. This article finds that LFO choices mainly affect small corporations' access to external equity capital, but have no significant impact on loan financing. Specifically, when a small corporation selects the C corporate legal form, the probability of obtaining new external equity is eight times higher compared to when it selects the S corporate legal form. Furthermore, the results suggest that better access to external equity investments, loosening business capital constraints, leads to better growth prospects for small C corporations. These empirical results have important macro‐economic implications on corporate financial and fiscal policies. (JEL G32, G38)     

Reweighted estimators for additive hazard model with censoring indicators missing at random

Survival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without reweighting. The proposed methods are illustrated by analyzing a dataset from a breast cancer study.