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