6,767 research outputs found
Low energy indoor network : deployment optimisation
This article considers what the minimum energy indoor access point deployment is in order to achieve a certain downlink quality-of-service. The article investigates two conventional multiple-access technologies, namely: LTE-femtocells and 802.11n Wi-Fi. This is done in a dynamic multi-user and multi-cell interference network. Our baseline results are reinforced by novel theoretical expressions. Furthermore, the work underlines the importance of considering optimisation when accounting for the capacity saturation of realistic modulation and coding schemes. The results in this article show that optimising the location of access points both within a building and within the individual rooms is critical to minimise the energy consumption
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
This paper proposes a set of new error criteria and learning approaches,
Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex
optimization problem in training deep neural networks (DNNs). Theoretically, we
demonstrate its effectiveness on global and local convexity lower-bounded by
the standard -norm error. By analyzing the gradient on the convexity index
, we explain the reason why to learn adaptively using
gradient descent works. In practice, we show how this method improves training
of deep neural networks to solve visual recognition tasks on the MNIST and
CIFAR-10 datasets. Without using pretraining or other tricks, we obtain results
comparable or superior to those reported in recent literature on the same tasks
using standard ConvNets + MSE/cross entropy. Performance on deep/shallow
multilayer perceptrons and Denoised Auto-encoders is also explored. ANRAT can
be combined with other quasi-Newton training methods, innovative network
variants, regularization techniques and other specific tricks in DNNs. Other
than unsupervised pretraining, it provides a new perspective to address the
non-convex optimization problem in DNNs.Comment: AAAI 2016, 0.39%~0.4% ER on MNIST with single 32-32-256-10 ConvNets,
code available at https://github.com/cauchyturing/ANRA
Bargaining, Revenue Sharing and Control Rights Allocation
In a two-period, double moral hazard model with incomplete contracting, this paper explores the relationship between revenue sharing and control rights. Specifically, we endogenize the allocation of both the income rights and the control rights and show why the two are often bundled together in the context of a two-party joint venture. Moreover, we study how the use of different bargaining solutions for the ex post contract renegotiation game may affect the optimal allocation of income and control rights. Our results can be used to explain the commonly observed ownership structures of equity joint ventures.
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