857 research outputs found
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ
A Novel Uplink Data Transmission Scheme For Small Packets In Massive MIMO System
Intelligent terminals often produce a large number of data packets of small
lengths. For these packets, it is inefficient to follow the conventional medium
access control (MAC) protocols because they lead to poor utilization of service
resources. We propose a novel multiple access scheme that targets massive
multiple-input multiple-output (MIMO) systems based on compressive sensing
(CS). We employ block precoding in the time domain to enable the simultaneous
transmissions of many users, which could be even more than the number of
receive antennas at the base station. We develop a block-sparse system model
and adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the
transmitted signals. Conditions for data recovery guarantees are identified and
numerical results demonstrate that our scheme is efficient for uplink small
packet transmission.Comment: IEEE/CIC ICCC 2014 Symposium on Signal Processing for Communication
Multiple Access for Small Packets Based on Precoding and Sparsity-Aware Detection
Modern mobile terminals often produce a large number of small data packets.
For these packets, it is inefficient to follow the conventional medium access
control protocols because of poor utilization of service resources. We propose
a novel multiple access scheme that employs block-spreading based precoding at
the transmitters and sparsity-aware detection schemes at the base station. The
proposed scheme is well suited for the emerging massive multiple-input
multiple-output (MIMO) systems, as well as conventional cellular systems with a
small number of base-station antennas. The transmitters employ precoding in
time domain to enable the simultaneous transmissions of many users, which could
be even more than the number of receive antennas at the base station. The
system is modeled as a linear system of equations with block-sparse unknowns.
We first adopt the block orthogonal matching pursuit (BOMP) algorithm to
recover the transmitted signals. We then develop an improved algorithm, named
interference cancellation BOMP (ICBOMP), which takes advantage of error
correction and detection coding to perform perfect interference cancellation
during each iteration of BOMP algorithm. Conditions for guaranteed data
recovery are identified. The simulation results demonstrate that the proposed
scheme can accommodate more simultaneous transmissions than conventional
schemes in typical small-packet transmission scenarios.Comment: submitted to IEEE Transactions on Wireless Communication
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