3,438 research outputs found
Melting of genomic DNA: predictive modeling by nonlinear lattice dynamics
The melting behavior of long, heterogeneous DNA chains is examined within the
framework of the nonlinear lattice dynamics based Peyrard-Bishop-Dauxois (PBD)
model. Data for the pBR322 plasmid and the complete T7 phage have been used to
obtain model fits and determine parameter dependence on salt content. Melting
curves predicted for the complete fd phage and the Y1 and Y2 fragments of the
X174 phage without any adjustable parameters are in good agreement with
experiment. The calculated probabilities for single base-pair opening are
consistent with values obtained from imino proton exchange experiments.Comment: 5 pages, 4 figures, to appear in Phys. Rev.
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
We present a new method to translate videos to commands for robotic
manipulation using Deep Recurrent Neural Networks (RNN). Our framework first
extracts deep features from the input video frames with a deep Convolutional
Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are
then used to encode the visual features and sequentially generate the output
words as the command. We demonstrate that the translation accuracy can be
improved by allowing a smooth transaction between two RNN layers and using the
state-of-the-art feature extractor. The experimental results on our new
challenging dataset show that our approach outperforms recent methods by a fair
margin. Furthermore, we combine the proposed translation module with the vision
and planning system to let a robot perform various manipulation tasks. Finally,
we demonstrate the effectiveness of our framework on a full-size humanoid robot
WALK-MAN
Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks
We present a new method to relocalize the 6DOF pose of an event camera solely
based on the event stream. Our method first creates the event image from a list
of events that occurs in a very short time interval, then a Stacked Spatial
LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is
composed of a CNN to learn deep features from the event images and a stack of
LSTM to learn spatial dependencies in the image feature space. We show that the
spatial dependency plays an important role in the relocalization task and the
SP-LSTM can effectively learn this information. The experimental results on a
publicly available dataset show that our approach generalizes well and
outperforms recent methods by a substantial margin. Overall, our proposed
method reduces by approx. 6 times the position error and 3 times the
orientation error compared to the current state of the art. The source code and
trained models will be released.Comment: 7 pages, 5 figure
- …