TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.Comment: 14 page

The Roles of Bank and Trade Credits: Theoretical Analysis and Empirical Evidence

This study investigates the roles of bank and trade credits in a supply chain with a capital-constrained retailer facing demand uncertainty. We evaluate the retailer\u27s optimal order quantity and the creditors\u27 optimal credit limits and interest rates in two scenarios. In the single-credit scenario, we find the retailer prefers trade credit, if the trade credit market is more competitive than the bank credit market; otherwise, the retailer\u27s preference of a specific credit type depends on the risk levels that the retailer would divert trade credit and bank credit to other risky investments. In the dual-credit scenario, if the bank credit market is more competitive than the trade credit market, the retailer first borrows bank credit prior to trade credit, but then switches to borrowing trade credit prior to bank credit as the retailer\u27s internal capital declines. In contrast, if the trade credit market is more competitive, the retailer borrows only trade credit. We further analytically prove that the two credits are complementary if the retailer\u27s internal capital is substantially low but become substitutable as the internal capital grows, and then empirically validate this prediction based on a panel of 674 firms in China over the period 2001–2007

Rational cost inefficiency in Chinese banks

According to a frequently cited finding by Berger et al (1993), X-inefficiency contributes 20% to cost-inefficiency in western banks. Empirical studies of Chinese banks tend to place cost-inefficiency in the region of 50%. Such estimates would suggest that Chinese banks suffer from gross cost inefficiency. Using a nonparametric bootstrapping method, this study decomposes cost-inefficiency in Chinese banks into X-inefficiency and allocative-inefficiency. It argues that allocative inefficiency is the optimal outcome of input resource allocation subject to enforced employment constraints. The resulting analysis suggests that allowing for rational allocative inefficiency; Chinese banks are no better or worse than their western counterparts

Multiple Positive Solutions of the Singular Boundary Value Problem for Second-Order Impulsive Differential Equations on the Half-Line

This paper uses a fixed point theorem in cones to investigate the multiple positive solutions of a boundary value problem for second-order impulsive singular differential equations on the half-line. The conditions for the existence of multiple positive solutions are established.This work is supported by the National Nature Science Foundation of P. R.China 10871063 and Scientific Research Fund of Hunan Provincial Education Department 07A038 , partially supported by Ministerio de Educacion y Ciencia and FEDER, Project MTM2007-61724, and by Xunta de Galicia and FEDER, project no.PGIDIT06PXIB207023PRS

AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation

In this paper, we study the problem of legal domain adaptation problem from an imbalanced source domain to a partial target domain. The task aims to improve legal judgment predictions for non-professional fact descriptions. We formulate this task as a partial-and-imbalanced domain adaptation problem. Though deep domain adaptation has achieved cutting-edge performance in many unsupervised domain adaptation tasks. However, due to the negative transfer of samples in non-shared classes, it is hard for current domain adaptation model to solve the partial-and-imbalanced transfer problem. In this work, we explore large-scale non-shared but related classes data in the source domain with a hierarchy weighting adaptation to tackle this limitation. We propose to embed a novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep learning model, which can jointly borrow sibling knowledge from non-shared classes to shared classes in the source domain and further transfer the shared classes knowledge from the source domain to the target domain. Experimental results show that our model outperforms the state-of-the-art algorithms.Comment: 13 pages, 15 figure