High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.Comment: 8 pages paper, 11 pages supplementary material, ICCV spotlight pape

Nonlinear Dynamics and Chaos in Fractional-Order Hopfield Neural Networks with Delay

A fractional-order two-neuron Hopfield neural network with delay is proposed based on the classic well-known Hopfield neural networks, and further, the complex dynamical behaviors of such a network are investigated. A great variety of interesting dynamical phenomena, including single-periodic, multiple-periodic, and chaotic motions, are found to exist. The existence of chaotic attractors is verified by the bifurcation diagram and phase portraits as well

Does Zero-leverage Policy Increase Inefficient Investment? - From The Perspective Of Lack Of Bank Creditors

Using a sample of up to 12023 firm-year observations across 2358 individual firms from 2007 to 2013, this paper examines whether zero-leverage policy increases firms’ inefficient investment from the perspective of lack of bank creditors. Due to the lack of bank creditor monitoring, zero-leverage policy leads to more serious information asymmetry and agency problems, which are the two types of frictions that affect investment efficiency. The empirical results show that zero-leverage policy indeed increases inefficient investment. Furthermore, we test whether external monitoring helps to mitigate the effects of zero-leverage policy on inefficient investment. Our findings suggest that the sensitivity between zero-leverage policy and inefficient investment will be lower in firms with strong external monitoring. Overall, the zero-leverage policy seems to be a key determinant of inefficient investment