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Sequence Classification Restricted Boltzmann Machines With Gated Units
For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)
Deep Learning Solutions for TanDEM-X-based Forest Classification
In the last few years, deep learning (DL) has been successfully and massively
employed in computer vision for discriminative tasks, such as image
classification or object detection. This kind of problems are core to many
remote sensing (RS) applications as well, though with domain-specific
peculiarities. Therefore, there is a growing interest on the use of DL methods
for RS tasks. Here, we consider the forest/non-forest classification problem
with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting
them to the specific task. Our experiments confirm the great potential of DL
methods for RS applications
On the Energy Efficiency of MapReduce Shuffling Operations in Data Centers
This paper aims to quantitatively measure the impact of different data centers networking topologies on the performance and energy efficiency of shuffling operations in MapReduce. Mixed Integer Linear Programming (MILP) models are utilized to optimize the shuffling in several data center topologies with electronic, hybrid, and all-optical switching while maximizing the throughput and reducing the power consumption. The results indicate that the networking topology has a significant impact on the performance of MapReduce. They also indicate that with comparable performance, optical-based data centers can achieve an average of 54% reduction in the energy consumption when compared to electronic switching data centers
The evolution of interdisciplinarity in physics research
Science, being a social enterprise, is subject to fragmentation into groups
that focus on specialized areas or topics. Often new advances occur through
cross-fertilization of ideas between sub-fields that otherwise have little
overlap as they study dissimilar phenomena using different techniques. Thus to
explore the nature and dynamics of scientific progress one needs to consider
the large-scale organization and interactions between different subject areas.
Here, we study the relationships between the sub-fields of Physics using the
Physics and Astronomy Classification Scheme (PACS) codes employed for
self-categorization of articles published over the past 25 years (1985-2009).
We observe a clear trend towards increasing interactions between the different
sub-fields. The network of sub-fields also exhibits core-periphery
organization, the nucleus being dominated by Condensed Matter and General
Physics. However, over time Interdisciplinary Physics is steadily increasing
its share in the network core, reflecting a shift in the overall trend of
Physics research.Comment: Published version, 10 pages, 8 figures + Supplementary Informatio
Practical issues for the implementation of survivability and recovery techniques in optical networks
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