337,433 research outputs found
Bayesian Grammar Induction for Language Modeling
We describe a corpus-based induction algorithm for probabilistic context-free
grammars. The algorithm employs a greedy heuristic search within a Bayesian
framework, and a post-pass using the Inside-Outside algorithm. We compare the
performance of our algorithm to n-gram models and the Inside-Outside algorithm
in three language modeling tasks. In two of the tasks, the training data is
generated by a probabilistic context-free grammar and in both tasks our
algorithm outperforms the other techniques. The third task involves
naturally-occurring data, and in this task our algorithm does not perform as
well as n-gram models but vastly outperforms the Inside-Outside algorithm.Comment: 8 pages, LaTeX, uses aclap.st
Control of laser wake field acceleration by plasma density profile
We show that both the maximum energy gain and the accelerated beam quality
can be efficiently controlled by the plasma density profile. Choosing a proper
density gradient one can uplift the dephasing limitation. When a periodic wake
field is exploited, the phase synchronism between the bunch of relativistic
particles and the plasma wave can be maintained over extended distances due to
the plasma density gradient. Putting electrons into the th wake period
behind the driving laser pulse, the maximum energy gain is increased by the
factor over that in the case of uniform plasma. The acceleration is
limited then by laser depletion rather than by dephasing. Further, we show that
the natural energy spread of the particle bunch acquired at the acceleration
stage can be effectively removed by a matched deceleration stage, where a
larger plasma density is used
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations
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