1,431 research outputs found
Expanding to outward foreign direct investment or not? A multi-dimensional analysis of entry mode transformation of Chinese private exporting firms
This research examines the factors determining whether or not exporting firms expand to outward foreign direct investment (OFDI) as part of their internationalisation strategy, using a recent survey of Chinese private-owned enterprises. We carry out a multi-dimensional analysis to investigate the impact of firm productivity, internal resources and the external environment on OFDI decisions, including both the decision to undertake OFDI and the volume of OFDI flows. It is found that productivity, technology-based capability, export experience, industry entry barriers, subnational institutions and intermediary institutional support affect firms’ OFDI decisions. The findings have important policy and managerial implications
Budget Feasible Mechanism Design: From Prior-Free to Bayesian
Budget feasible mechanism design studies procurement combinatorial auctions
where the sellers have private costs to produce items, and the
buyer(auctioneer) aims to maximize a social valuation function on subsets of
items, under the budget constraint on the total payment. One of the most
important questions in the field is "which valuation domains admit truthful
budget feasible mechanisms with `small' approximations (compared to the social
optimum)?" Singer showed that additive and submodular functions have such
constant approximations. Recently, Dobzinski, Papadimitriou, and Singer gave an
O(log^2 n)-approximation mechanism for subadditive functions; they also
remarked that: "A fundamental question is whether, regardless of computational
constraints, a constant-factor budget feasible mechanism exists for subadditive
functions."
We address this question from two viewpoints: prior-free worst case analysis
and Bayesian analysis. For the prior-free framework, we use an LP that
describes the fractional cover of the valuation function; it is also connected
to the concept of approximate core in cooperative game theory. We provide an
O(I)-approximation mechanism for subadditive functions, via the worst case
integrality gap I of LP. This implies an O(log n)-approximation for subadditive
valuations, O(1)-approximation for XOS valuations, and for valuations with a
constant I. XOS valuations are an important class of functions that lie between
submodular and subadditive classes. We give another polynomial time O(log
n/loglog n) sub-logarithmic approximation mechanism for subadditive valuations.
For the Bayesian framework, we provide a constant approximation mechanism for
all subadditive functions, using the above prior-free mechanism for XOS
valuations as a subroutine. Our mechanism allows correlations in the
distribution of private information and is universally truthful.Comment: to appear in STOC 201
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
Recurrent Multimodal Interaction for Referring Image Segmentation
In this paper we are interested in the problem of image segmentation given
natural language descriptions, i.e. referring expressions. Existing works
tackle this problem by first modeling images and sentences independently and
then segment images by combining these two types of representations. We argue
that learning word-to-image interaction is more native in the sense of jointly
modeling two modalities for the image segmentation task, and we propose
convolutional multimodal LSTM to encode the sequential interactions between
individual words, visual information, and spatial information. We show that our
proposed model outperforms the baseline model on benchmark datasets. In
addition, we analyze the intermediate output of the proposed multimodal LSTM
approach and empirically explain how this approach enforces a more effective
word-to-image interaction.Comment: To appear in ICCV 2017. See http://www.cs.jhu.edu/~cxliu/ for code
and supplementary materia
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