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Covalent Self-labeling of Tagged Proteins with Chemical Fluorescent Dyes in BY-2 Cells and Arabidopsis Seedlings
BY-2 cells Arabidopsis SNAP-tag live cell imaging microtubules endocytosis PIN2 auxin transporter
2023/12/21
Synthetic chemical fluorescent dyes promise to be useful for many applications in biology. Covalent, targeted labeling, such as with a SNAP-tag, uses synthetic dyes to label specific proteins in vivo ...
Spatial Variations of Soil N2 and N2O Emissions from a Temperate Forest: Quantified by the In Situ 15N Labeling Method
15N labeling dinitrogen emission in situ N2O/(N2O + N2) ratio temperate forest water gradient
2023/12/1
Emissions of dinitrogen (N2) and nitrous oxide (N2O) from soil are important components of the global nitrogen cycle. Soil N2O emissions from terrestrial ecosystems have been well studied. However, pa...
SEMANTIC PHOTOGRAMMETRY – BOOSTING IMAGE-BASED 3D RECONSTRUCTION WITH SEMANTIC LABELING
image-based 3D reconstruction label transfer semantic photogrammetry dense image matching
2019/3/4
Automatic semantic segmentation of images is becoming a very prominent research field with many promising and reliable solutions already available. Labelled images as input for the photogrammetric pip...
3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS
3D semantic labelling ALS data random forest domain adaption decision fusion
2018/5/15
Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting doma...
FDA regulations regarding iodine addition to foods and labeling of foods containing added iodine
dietary supplements foods infant formula iodine labeling
2018/12/14
The US Food and Drug Administration (FDA) regulates the addition of iodine to infant formulas, the iodization of salt, and the addition of salt and iodine to foods. The required amount of iodine in in...
IMAGE LABELING FOR LIDAR INTENSITY IMAGE USING K-NN OF FEATURE OBTAINED BY CONVOLUTIONAL NEURAL NETWORK
Image labeling Convolutional neural network K-nearest neighbour and LIDAR intensity image
2016/7/28
We propose an image labeling method for LIDAR intensity image obtained by Mobile Mapping System (MMS) using K-Nearest Neighbor (KNN) of feature obtained by Convolutional Neural Network (CNN). Image la...
Semantic Role Labeling of Implicit Arguments for Nominal Predicates
Semantic Role Labeling Implicit Arguments Nominal Predicates
2015/9/10
Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have...
Large-scale annotated corpora are a prerequisite to developing high-performance semantic role
labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and
may not be ...
Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation
Syntax-Based Machine Translation Re-labeling,
2015/9/8
his article shows that the structure of bilingual material from standard parsing and alignment
tools is not optimal for training syntax-based statistical machine translation (SMT) systems.
We presen...
The availability of large scale data sets of manually annotated predicate-argument structures has recently favored the use of machine learning approaches to the design of automated semantic role label...
In this article we report work on Chinese semantic role labeling, taking advantage of two recently completed corpora, the Chinese PropBank, a semantically annotated corpus of Chinese verbs, and the Ch...
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
Syntactic Parsing Inference Semantic Role Labeling
2015/9/6
We present a general framework for semantic role labeling. The framework combines a machine-learning technique with an integer linear programming-based inference procedure, which incorporates linguist...
Semantic Role Labeling:An Introduction to the Special Issue
Semantic Role Labeling Special Issue
2015/9/6
Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been st...
We present a model for semantic role labeling that effectively captures the linguistic intuition that a semantic argument frame is a joint structure, with strong dependencies among the arguments. We s...
Most semantic role labeling (SRL) research has been focused on training and evaluating on the same corpus. This strategy, although appropriate for initiating research, can lead to overtraining to the ...