205 research outputs found
Can pro-environmental behavior increase farmers’ income?—Evidence from arable land quality protection practices in China
In China, agricultural non-point source pollution is one of the key
factors limiting farmers’ income growth, and pro-environmental
behavior can address agricultural surface pollution. Based on field
survey data from 591 farmers in Xinjiang, China, this study empirically
estimates the impact of pro-environmental behavior on
farmers’ income growth. The results show that pro-environmental
behavior plays a significant positive role in increasing farmers’
income, and the positive effect continues in the long run.
Specifically, pro-environmental behavior can optimize the allocation
of agricultural production factors, thus resulting in farmers’
income growth. The mechanism analysis shows that pro-environmental
behavior affects farmers’ income growth by promoting
the increase in the size of arable land and farmers’ willingness to
transfer their land in the future. These findings indicate that a
sound reward–punishment system for pro-environmental behavior
should be established; training on pro-environmental behavior
should be strengthened, and a mechanism for linking the benefits
of pro-environmental behavior among stakeholders should be
constructed
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually
contaminated by severe and arbitrary fetal and maternal motion. Hence, stable
and robust motion correction is necessary to reconstruct high-resolution 3D
fetal brain volume for clinical diagnosis and quantitative analysis. However,
the conventional registration-based correction has a limited capture range and
is insufficient for detecting relatively large motions. Here, we present a
novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM)
correction of the multi-slice fetal brain MRI. It learns the sequential motion
from multiple stacks of slices and integrates the features between 2D slices
and reconstructed 3D volume using affinity fusion, which resembles the
iterations between slice-to-volume registration and volumetric reconstruction
in the regular pipeline. The method accurately estimates the motion regardless
of brain orientations and outperforms other state-of-the-art learning-based
methods on the simulated motion-corrupted data, with a 48.4% reduction of mean
absolute error for rotation and 61.3% for displacement. We then incorporated
AFFIRM into the multi-resolution slice-to-volume registration and tested it on
the real-world fetal MRI scans at different gestation stages. The results
indicated that adding AFFIRM to the conventional pipeline improved the success
rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%
Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR
We present techniques to measure crop heights using a 3D Light Detection and
Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the
height of plants is crucial to monitor their overall health and growth cycles,
especially for high-throughput plant phenotyping. We present a methodology for
extracting plant heights from 3D LiDAR point clouds, specifically focusing on
plot-based phenotyping environments. We also present a toolchain that can be
used to create phenotyping farms for use in Gazebo simulations. The tool
creates a randomized farm with realistic 3D plant and terrain models. We
conducted a series of simulations and hardware experiments in controlled and
natural settings. Our algorithm was able to estimate the plant heights in a
field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is
the first such dataset for 3D LiDAR from an airborne robot over a wheat field.
The developed simulation toolchain, algorithmic implementation, and datasets
can be found on the GitHub repository located at
https://github.com/hsd1121/PointCloudProcessing.Comment: 8 pages, 10 figures, 1 table, Accepted to IROS 202
- …