690 research outputs found
Bean germplasm conservation based on seed drying with silica gel and low moisture storage
Preservation of germplasm collections with low temperature storage is problematic because of power failures and equipment breakdown. Low moisture storage is an alternative to low temperature storage for medium-term germplasm conservation of seeds of most crops. Seed drying using silica gel for medium-term storage of bean seed was investigated. Seeds of two bean cultivars were dried for 50 days with silica gel in a desiccator experiment using a gel to seed ratio of 1:2. The final moisture content was 6.1 and 6.6 percent for the two cultivars. Dry seeds were stored in recycled glass soda bottles with screw caps sealed with candle wax at 25 degrees C for one year. The seed moisture content remained constant confirming that recycled glass soda bottles can be used as inexpensive seed storage containers. Germination rates after one year of storage were 97.5 and 100 percent for the two cultivars. It is expected that the seed can be kept in glass bottles for 10-20 years (mid-term storage). In order to dry larger amounts of seed, a drying facility using silica gel in an air-tight PVC drum was developed. Procedures were developed for collection, characterization and maintenance of bean germplasm collections, as well as for data management
Inverse Seesaw Neutrino Mass from Lepton Triplets in the U(1)_Sigma Model
The inverse seesaw mechanism of neutrino mass, i.e. m_nu =
(m_D^2/m_N^2)epsilon_L where epsilon_L is small, is discussed in the context of
the U(1)_Sigma model. This is a gauge extension of the Standard Model of
particle interactions with lepton triplets (Sigma^+,Sigma^),Sigma^-) as (Type
III) seesaw anchors for obtaining small Majorana neutrino masses.Comment: 7 pages, no figur
Neutrino Masses in Supersymmetry: R-Parity and Leptogenesis
In the supersymmetric standard model of particle interactions, R-parity
nonconservation is often invoked to obtain nonzero neutrino masses. We point
out here that such interactions of the supersymmetric particles would erase any
pre-existing lepton or baryon asymmetry of the universe before the electroweak
phase transition through the violating sphaleron processes. We then
show how neutrino masses may be obtained in supersymmetry (assuming R-parity
conservation) together with successful leptogenesis and predict the possible
existence of new observable particles.Comment: LATEX, 12 page
Towards Semantic Fast-Forward and Stabilized Egocentric Videos
The emergence of low-cost personal mobiles devices and wearable cameras and
the increasing storage capacity of video-sharing websites have pushed forward a
growing interest towards first-person videos. Since most of the recorded videos
compose long-running streams with unedited content, they are tedious and
unpleasant to watch. The fast-forward state-of-the-art methods are facing
challenges of balancing the smoothness of the video and the emphasis in the
relevant frames given a speed-up rate. In this work, we present a methodology
capable of summarizing and stabilizing egocentric videos by extracting the
semantic information from the frames. This paper also describes a dataset
collection with several semantically labeled videos and introduces a new
smoothness evaluation metric for egocentric videos that is used to test our
method.Comment: Accepted for publication and presented in the First International
Workshop on Egocentric Perception, Interaction and Computing at European
Conference on Computer Vision (EPIC@ECCV) 201
Scatteract: Automated extraction of data from scatter plots
Charts are an excellent way to convey patterns and trends in data, but they
do not facilitate further modeling of the data or close inspection of
individual data points. We present a fully automated system for extracting the
numerical values of data points from images of scatter plots. We use deep
learning techniques to identify the key components of the chart, and optical
character recognition together with robust regression to map from pixels to the
coordinate system of the chart. We focus on scatter plots with linear scales,
which already have several interesting challenges. Previous work has done fully
automatic extraction for other types of charts, but to our knowledge this is
the first approach that is fully automatic for scatter plots. Our method
performs well, achieving successful data extraction on 89% of the plots in our
test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page
Human Pose Estimation using Deep Consensus Voting
In this paper we consider the problem of human pose estimation from a single
still image. We propose a novel approach where each location in the image votes
for the position of each keypoint using a convolutional neural net. The voting
scheme allows us to utilize information from the whole image, rather than rely
on a sparse set of keypoint locations. Using dense, multi-target votes, not
only produces good keypoint predictions, but also enables us to compute
image-dependent joint keypoint probabilities by looking at consensus voting.
This differs from most previous methods where joint probabilities are learned
from relative keypoint locations and are independent of the image. We finally
combine the keypoints votes and joint probabilities in order to identify the
optimal pose configuration. We show our competitive performance on the MPII
Human Pose and Leeds Sports Pose datasets
Accomplishments of participatory research for systems improvement in Iganga District, Uganda 1993 to 1997
Allowable Low-Energy E_6 Subgroups from Leptogenesis
There are only two viable low-energy subgroups: or , which would not erase any preexisting lepton asymmetry of
the Universe that may have been created by the decay of heavy singlet
(right-handed) neutrinos or any other mechanism. They are also the two most
favored subgroups from a recent analysis of present neutral-current data.
We study details of the leptogenesis, as well as some salient experimental
signatures of the two models.Comment: 12 page
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using the predicted coordinates. Usually, the CNN is trained with ground truth
scene coordinates, but it has also been shown that the network can discover 3D
scene geometry automatically by minimizing single-view reprojection loss.
However, due to the deficiencies of the reprojection loss, the network needs to
be carefully initialized. In this paper, we present a new angle-based
reprojection loss, which resolves the issues of the original reprojection loss.
With this new loss function, the network can be trained without careful
initialization, and the system achieves more accurate results. The new loss
also enables us to utilize available multi-view constraints, which further
improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
This paper proposes a two-view deterministic geometric model fitting method,
termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure
data. SDF starts from superpixel segmentation, which effectively captures prior
information of feature appearances. The feature appearances are beneficial to
reduce the computational complexity for deterministic fitting methods. SDF also
includes two original elements, i.e., a deterministic sampling algorithm and a
novel model selection algorithm. The two algorithms are tightly coupled to
boost the performance of SDF in both speed and accuracy. Specifically, the
proposed sampling algorithm leverages the grouping cues of superpixels to
generate reliable and consistent hypotheses. The proposed model selection
algorithm further makes use of desirable properties of the generated
hypotheses, to improve the conventional fit-and-remove framework for more
efficient and effective performance. The key characteristic of SDF is that it
can efficiently and deterministically estimate the parameters of model
instances in multi-structure data. Experimental results demonstrate that the
proposed SDF shows superiority over several state-of-the-art fitting methods
for real images with single-structure and multiple-structure data.Comment: Accepted by European Conference on Computer Vision (ECCV
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