489 research outputs found
The Margin Abatement Costs of CO2 in Chinese industrial sectors
AbstractUsing the directional distance function estimating by a non-parametric method, this paper measured shadow prices indicating the margin abatement costs (MACs) of CO2 emissions of China's industrial sectors. The results show that the MACs are within 0.2 thousand Yuan per ton to 120.3 thousand Yuan per ton, differentiating among sectors. In average, the MACs of heavy and chemical industries are lower than that of light and high-tech industries
Complexity and Approximation Results for the Min-Sum and Min-Max Disjoint Paths Problems
Given a graph G=(V, E) and k source-sink pairs (s1, t1), …, (sk, tk) with each si, ti V, the Min-Sum Disjoint Paths problem asks to find k disjoint paths connecting all the source-sink pairs with minimized total length, while the Min-Max Disjoint Paths problem asks for k disjoint paths connecting all the source-sink pairs with minimized length of the longest path. We show that the weighted Min-Sum Disjoint Paths problem is FPNP-complete in general graphs, and the unweighted Min-Sum Disjoint Paths problem and the unweighted Min-Max Disjoint Paths problem cannot be approximated within m(m1-1) for any constant > 0 even in planar graphs, assuming P P NP, where m is the number of edges in G. We give for the first time a simple bicriteria approximation algorithm for the unweighted Min-Max Edge-Disjoint Paths problem and the weighted Min-Sum Edge-Disjoint Paths problem, w
Vertical-slice ocean tomography with seismic waves
Seismically generated sound waves that propagate through the ocean are used
to infer temperature anomalies and their vertical structure in the deep East
Indian Ocean. These T waves are generated by earthquakes off Sumatra and
received by hydrophone stations off Diego Garcia and Cape Leeuwin. Between
repeating earthquakes, a T wave's travel time changes in response to
temperature anomalies along the wave's path. What part of the water column the
travel time is sensitive to depends on the frequency of the wave, so measuring
travel time changes at a few low frequencies constrains the vertical structure
of the inferred temperature anomalies. These measurements reveal anomalies due
to equatorial waves, mesoscale eddies, and decadal warming trends. By providing
direct constraints on basin-scale averages with dense sampling in time, these
data complement previous point measurements that alias local and transient
temperature anomalies
Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
Lobster eye telescopes are ideal monitors to detect X-ray transients, because
they could observe celestial objects over a wide field of view in X-ray band.
However, images obtained by lobster eye telescopes are modified by their unique
point spread functions, making it hard to design a high efficiency target
detection algorithm. In this paper, we integrate several machine learning
algorithms to build a target detection framework for data obtained by lobster
eye telescopes. Our framework would firstly generate two 2D images with
different pixel scales according to positions of photons on the detector. Then
an algorithm based on morphological operations and two neural networks would be
used to detect candidates of celestial objects with different flux from these
2D images. At last, a random forest algorithm will be used to pick up final
detection results from candidates obtained by previous steps. Tested with
simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe,
our detection framework could achieve over 94% purity and over 90% completeness
for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than
94% purity and moderate completeness for targets with lower flux at acceptable
time cost. The framework proposed in this paper could be used as references for
data processing methods developed for other lobster eye X-ray telescopes.Comment: Accepted by the APJS Journal. Full source code could be downloaded
from the China VO with DOI of https://doi.org/10.12149/101175. Docker version
of the code could be obtained under request to the corresponding autho
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