4,541 research outputs found
High brightness fully coherent X-ray amplifier seeded by a free-electron laser oscillator
X-ray free-electron laser oscillator (XFELO) is expected to be a cutting edge
tool for fully coherent X-ray laser generation, and undulator taper technique
is well-known for considerably increasing the efficiency of free-electron
lasers (FELs). In order to combine the advantages of these two schemes, FEL
amplifier seeded by XFELO is proposed by simply using a chirped electron beam.
With the right choice of the beam parameters, the bunch tail is within the gain
bandwidth of XFELO, and lase to saturation, which will be served as a seeding
for further amplification. Meanwhile, the bunch head which is outside the gain
bandwidth of XFELO, is preserved and used in the following FEL amplifier. It is
found that the natural "double-horn" beam current as well as residual energy
chirp from chicane compressor are quite suitable for the new scheme. Inheriting
the advantages from XFELO seeding and undulator tapering, it is feasible to
generate nearly terawatt level, fully coherent X-ray pulses with unprecedented
shot-to-shot stability, which might open up new scientific opportunities in
various research fields.Comment: 8 pages, 8 figure
Object Detection in Videos with Tubelet Proposal Networks
Object detection in videos has drawn increasing attention recently with the
introduction of the large-scale ImageNet VID dataset. Different from object
detection in static images, temporal information in videos is vital for object
detection. To fully utilize temporal information, state-of-the-art methods are
based on spatiotemporal tubelets, which are essentially sequences of associated
bounding boxes across time. However, the existing methods have major
limitations in generating tubelets in terms of quality and efficiency.
Motion-based methods are able to obtain dense tubelets efficiently, but the
lengths are generally only several frames, which is not optimal for
incorporating long-term temporal information. Appearance-based methods, usually
involving generic object tracking, could generate long tubelets, but are
usually computationally expensive. In this work, we propose a framework for
object detection in videos, which consists of a novel tubelet proposal network
to efficiently generate spatiotemporal proposals, and a Long Short-term Memory
(LSTM) network that incorporates temporal information from tubelet proposals
for achieving high object detection accuracy in videos. Experiments on the
large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed
framework for object detection in videos.Comment: CVPR 201
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