772 research outputs found
Rate Coefficients for the Collisional Excitation of Molecules: Estimates from an Artificial Neural Network
An artificial neural network (ANN) is investigated as a tool for estimating
rate coefficients for the collisional excitation of molecules. The performance
of such a tool can be evaluated by testing it on a dataset of
collisionally-induced transitions for which rate coefficients are already
known: the network is trained on a subset of that dataset and tested on the
remainder. Results obtained by this method are typically accurate to within a
factor ~ 2.1 (median value) for transitions with low excitation rates and ~ 1.7
for those with medium or high excitation rates, although 4% of the ANN outputs
are discrepant by a factor of 10 more. The results suggest that ANNs will be
valuable in extrapolating a dataset of collisional rate coefficients to include
high-lying transitions that have not yet been calculated. For the asymmetric
top molecules considered in this paper, the favored architecture is a
cascade-correlation network that creates 16 hidden neurons during the course of
training, with 3 input neurons to characterize the nature of the transition and
one output neuron to provide the logarithm of the rate coefficient.Comment: 23 pages including 9 figures. Accepted for publication in Ap
Multimodal Deep Learning for Robust RGB-D Object Recognition
Robust object recognition is a crucial ingredient of many, if not all,
real-world robotics applications. This paper leverages recent progress on
Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture
for object recognition. Our architecture is composed of two separate CNN
processing streams - one for each modality - which are consecutively combined
with a late fusion network. We focus on learning with imperfect sensor data, a
typical problem in real-world robotics tasks. For accurate learning, we
introduce a multi-stage training methodology and two crucial ingredients for
handling depth data with CNNs. The first, an effective encoding of depth
information for CNNs that enables learning without the need for large depth
datasets. The second, a data augmentation scheme for robust learning with depth
images by corrupting them with realistic noise patterns. We present
state-of-the-art results on the RGB-D object dataset and show recognition in
challenging RGB-D real-world noisy settings.Comment: Final version submitted to IROS'2015, results unchanged,
reformulation of some text passages in abstract and introductio
Learning and Transfer of Modulated Locomotor Controllers
We study a novel architecture and training procedure for locomotion tasks. A
high-frequency, low-level "spinal" network with access to proprioceptive
sensors learns sensorimotor primitives by training on simple tasks. This
pre-trained module is fixed and connected to a low-frequency, high-level
"cortical" network, with access to all sensors, which drives behavior by
modulating the inputs to the spinal network. Where a monolithic end-to-end
architecture fails completely, learning with a pre-trained spinal module
succeeds at multiple high-level tasks, and enables the effective exploration
required to learn from sparse rewards. We test our proposed architecture on
three simulated bodies: a 16-dimensional swimming snake, a 20-dimensional
quadruped, and a 54-dimensional humanoid. Our results are illustrated in the
accompanying video at https://youtu.be/sboPYvhpraQComment: Supplemental video available at https://youtu.be/sboPYvhpra
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control
policies directly from high-dimensional sensory input using reinforcement
learning. The model is a convolutional neural network, trained with a variant
of Q-learning, whose input is raw pixels and whose output is a value function
estimating future rewards. We apply our method to seven Atari 2600 games from
the Arcade Learning Environment, with no adjustment of the architecture or
learning algorithm. We find that it outperforms all previous approaches on six
of the games and surpasses a human expert on three of them.Comment: NIPS Deep Learning Workshop 201
Persistent painless hemospermia due to metastatic melanoma of the right seminal vesicle
Background
Metastatic melanoma of the seminal vesicles is a very rare clinical entity and has been reported only once until today in a patient suffering from concomitant HIV infection 12 years ago.
Case presentation
We report a case of persistent, painless hemospermia in a young Caucasian caused by metastatic malignant melanoma of the right seminal vesicle. The diagnosis was established by magnetic resonance imaging and transrectal ultrasound-guided biopsy. In the subsequent diagnostic workup the primary location of the tumor remained unknown but concomitant pulmonary, hepatic and supraclavicular lymph node metastases have been detected. Despite immediate chemotherapy initiation the patient finally succumbed to his progressive disease six months later.
Conclusions
Malignant melanoma should be considered as a rare differential diagnosis of hemospermia after common causes have been ruled out
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