117 research outputs found
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
We present a neural-network-based architecture for 3D point cloud denoising
called neural projection denoising (NPD). In our previous work, we proposed a
two-stage denoising algorithm, which first estimates reference planes and
follows by projecting noisy points to estimated reference planes. Since the
estimated reference planes are inevitably noisy, multi-projection is applied to
stabilize the denoising performance. NPD algorithm uses a neural network to
estimate reference planes for points in noisy point clouds. With more accurate
estimations of reference planes, we are able to achieve better denoising
performances with only one-time projection. To the best of our knowledge, NPD
is the first work to denoise 3D point clouds with deep learning techniques. To
conduct the experiments, we sample 40000 point clouds from the 3D data in
ShapeNet to train a network and sample 350 point clouds from the 3D data in
ModelNet10 to test. Experimental results show that our algorithm can estimate
normal vectors of points in noisy point clouds. Comparing to five competitive
methods, the proposed algorithm achieves better denoising performance and
produces much smaller variances
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling
We study signal recovery on graphs based on two sampling strategies: random
sampling and experimentally designed sampling. We propose a new class of smooth
graph signals, called approximately bandlimited, which generalizes the
bandlimited class and is similar to the globally smooth class. We then propose
two recovery strategies based on random sampling and experimentally designed
sampling. The proposed recovery strategy based on experimentally designed
sampling is similar to the leverage scores used in the matrix approximation. We
show that while both strategies are unbiased estimators for the low-frequency
components, the convergence rate of experimentally designed sampling is much
faster than that of random sampling when a graph is irregular. We validate the
proposed recovery strategies on three specific graphs: a ring graph, an
Erd\H{o}s-R\'enyi graph, and a star graph. The simulation results support the
theoretical analysis.Comment: Correct some typo
CORPORATE CASH HOLDINGS: STUDY OF CHINESE FIRMS
This study focuses on the determinants of cash holdings in the period of 2003-2012 for Chinese manufacturing industry and how Chinese firms manage cash holdings. In general, when firms are more financially constrained, they are more likely to hold more cash. According to our investigation, firms with lower leverage, less net working capital (NWC), and lower capital expenditures, are more likely to increase cash holdings. In addition, China has different economic environment from developed countries, most of manufacturing firms are state owned; thus, the government policies influence the motivations of firms
Hypergraph Structure Inference From Data Under Smoothness Prior
Hypergraphs are important for processing data with higher-order relationships
involving more than two entities. In scenarios where explicit hypergraphs are
not readily available, it is desirable to infer a meaningful hypergraph
structure from the node features to capture the intrinsic relations within the
data. However, existing methods either adopt simple pre-defined rules that fail
to precisely capture the distribution of the potential hypergraph structure, or
learn a mapping between hypergraph structures and node features but require a
large amount of labelled data, i.e., pre-existing hypergraph structures, for
training. Both restrict their applications in practical scenarios. To fill this
gap, we propose a novel smoothness prior that enables us to design a method to
infer the probability for each potential hyperedge without labelled data as
supervision. The proposed prior indicates features of nodes in a hyperedge are
highly correlated by the features of the hyperedge containing them. We use this
prior to derive the relation between the hypergraph structure and the node
features via probabilistic modelling. This allows us to develop an unsupervised
inference method to estimate the probability for each potential hyperedge via
solving an optimisation problem that has an analytical solution. Experiments on
both synthetic and real-world data demonstrate that our method can learn
meaningful hypergraph structures from data more efficiently than existing
hypergraph structure inference methods
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