10,049 research outputs found
Understanding Convolution for Semantic Segmentation
Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical value. First, we design dense upsampling convolution
(DUC) to generate pixel-level prediction, which is able to capture and decode
more detailed information that is generally missing in bilinear upsampling.
Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
phase. This framework 1) effectively enlarges the receptive fields (RF) of the
network to aggregate global information; 2) alleviates what we call the
"gridding issue" caused by the standard dilated convolution operation. We
evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
state-of-art result of 80.1% mIOU in the test set at the time of submission. We
also have achieved state-of-the-art overall on the KITTI road estimation
benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
found at https://github.com/TuSimple/TuSimple-DUC .Comment: WACV 2018. Updated acknowledgements. Source code:
https://github.com/TuSimple/TuSimple-DU
Molecular docking via quantum approximate optimization algorithm
Molecular docking plays a pivotal role in drug discovery and precision
medicine, enabling us to understand protein functions and advance novel
therapeutics. Here, we introduce a potential alternative solution to this
problem, the digitized-counterdiabatic quantum approximate optimization
algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a
quantum computer. Our method was applied to analyze diverse biological systems,
including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with
piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with
JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate
and biologically relevant docking results, especially for larger molecular
docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware
compatibility in the noisy intermediate-scale quantum era, indicating their
potential for efficient implementation under practical docking scenarios. Our
findings underscore quantum computing's potential in drug discovery and offer
valuable insights for optimizing protein-ligand docking processes.Comment: 10 pages, 5 figures, All comments are welcom
Quasi-Periodic Variations in X-ray Emission and Long-Term Radio Observations: Evidence for a Two-Component Jet in Sw J1644+57
The continued observations of Sw J1644+57 in X-ray and radio bands
accumulated a rich data set to study the relativistic jet launched in this
tidal disruption event. The X-ray light curve of Sw J1644+57 from 5-30 days
presents two kinds of quasi-periodic variations: a 200 second quasi-periodic
oscillation (QPO) and a 2.7-day quasi-periodic variation. The latter has been
interpreted by a precessing jet launched near the Bardeen-Petterson radius of a
warped disk. Here we suggest that the 200s QPO could be associated with
a second, narrower jet sweeping the observer line-of-sight periodically, which
is launched from a spinning black hole in the misaligned direction with respect
to the black hole's angular momentum. In addition, we show that this
two-component jet model can interpret the radio light curve of the event,
especially the re-brightening feature starting days after the
trigger. From the data we infer that inner jet may have a Lorentz factor of
and a kinetic energy of , while the outer jet may have a Lorentz factor of
and a kinetic energy of .Comment: 11 pages, 7 figures, accepted for publication in Ap
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