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 $B + L$ 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

Allowable Low-Energy E_6 Subgroups from Leptogenesis

There are only two viable low-energy $E_6$ subgroups: $SU(3)_C \times SU(2)_L \times U(1)_Y \times U(1)_N$ or $SU(3)_C \times SU(2)_L \times SU(2)'_R \times U(1)_{Y_L + Y'_R}$, 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 $E_6$ 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