65 research outputs found
Joint Event Extraction via Structural Semantic Matching
Event Extraction (EE) is one of the essential tasks in information
extraction, which aims to detect event mentions from text and find the
corresponding argument roles. The EE task can be abstracted as a process of
matching the semantic definitions and argument structures of event types with
the target text. This paper encodes the semantic features of event types and
makes structural matching with target text. Specifically, Semantic Type
Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also,
the Joint Structural Semantic Matching (JSSM) model is built to jointly perform
event detection and argument extraction tasks through a bidirectional attention
layer. The experimental results on the ACE2005 dataset indicate that our model
achieves a significant performance improvemen
Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation
Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model
Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
Neural view synthesis (NVS) is one of the most successful techniques for
synthesizing free viewpoint videos, capable of achieving high fidelity from
only a sparse set of captured images. This success has led to many variants of
the techniques, each evaluated on a set of test views typically using image
quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research
on how NVS methods perform with respect to perceived video quality. We present
the first study on perceptual evaluation of NVS and NeRF variants. For this
study, we collected two datasets of scenes captured in a controlled lab
environment as well as in-the-wild. In contrast to existing datasets, these
scenes come with reference video sequences, allowing us to test for temporal
artifacts and subtle distortions that are easily overlooked when viewing only
static images. We measured the quality of videos synthesized by several NVS
methods in a well-controlled perceptual quality assessment experiment as well
as with many existing state-of-the-art image/video quality metrics. We present
a detailed analysis of the results and recommendations for dataset and metric
selection for NVS evaluation
PyPose: A Library for Robot Learning with Physics-based Optimization
Deep learning has had remarkable success in robotic perception, but its
data-centric nature suffers when it comes to generalizing to ever-changing
environments. By contrast, physics-based optimization generalizes better, but
it does not perform as well in complicated tasks due to the lack of high-level
semantic information and the reliance on manual parametric tuning. To take
advantage of these two complementary worlds, we present PyPose: a
robotics-oriented, PyTorch-based library that combines deep perceptual models
with physics-based optimization techniques. Our design goal for PyPose is to
make it user-friendly, efficient, and interpretable with a tidy and
well-organized architecture. Using an imperative style interface, it can be
easily integrated into real-world robotic applications. Besides, it supports
parallel computing of any order gradients of Lie groups and Lie algebras and
-order optimizers, such as trust region methods. Experiments
show that PyPose achieves 3-20 speedup in computation compared to
state-of-the-art libraries. To boost future research, we provide concrete
examples across several fields of robotics, including SLAM, inertial
navigation, planning, and control
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Research evaluation of mechanical cutting technology of mulberry branch
China is the world's largest silkworm cocoon and raw silk producer and exporter. However, as the labor price increases quickly, the sericulture production costs increases, the comparative benefits of sericulture production decreases, and the farmers' enthusiasm in mulberry planting becomes lower. To solve the problem, aiming at the low level of mechanical harvesting and cutting technology of mulberry branch in China, on the basis of discussing the cutting damage forms of mulberry stool in zhe progress of mechanical harvesting muiberry branch, the existing problems and the present situation of the research of cutting technology of mulberry branch and others crop stalks are analyzed. The developmental trend of the mechanical cutting technology of mulberry branch is put formward
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