391 research outputs found
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
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