96 research outputs found
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Evidence plays a crucial role in automated fact-checking. When verifying
real-world claims, existing fact-checking systems either assume the evidence
sentences are given or use the search snippets returned by the search engine.
Such methods ignore the challenges of collecting evidence and may not provide
sufficient information to verify real-world claims. Aiming at building a better
fact-checking system, we propose to incorporate full text from source documents
as evidence and introduce two enriched datasets. The first one is a
multilingual dataset, while the second one is monolingual (English). We further
develop a latent variable model to jointly extract evidence sentences from
documents and perform claim verification. Experiments indicate that including
source documents can provide sufficient contextual clues even when gold
evidence sentences are not annotated. The proposed system is able to achieve
significant improvements upon best-reported models under different settings.Comment: Fixed minor issues, 11 page
Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF
Recognizing useful named entities plays a vital role in medical information
processing, which helps drive the development of medical area research. Deep
learning methods have achieved good results in medical named entity recognition
(NER). However, we find that existing methods face great challenges when
dealing with the nested named entities. In this work, we propose a novel
method, referred to as ASAC, to solve the dilemma caused by the nested
phenomenon, in which the core idea is to model the dependency between different
categories of entity recognition. The proposed method contains two key modules:
the adaptive shared (AS) part and the attentive conditional random field (ACRF)
module. The former part automatically assigns adaptive weights across each task
to achieve optimal recognition accuracy in the multi-layer network. The latter
module employs the attention operation to model the dependency between
different entities. In this way, our model could learn better entity
representations by capturing the implicit distinctions and relationships
between different categories of entities. Extensive experiments on public
datasets verify the effectiveness of our method. Besides, we also perform
ablation analyses to deeply understand our methods
Materialism, Social Stratification, and Ethics: Evidence from SME Owners in China
Purpose: The study of business ethics has seldom shed light on small- and medium-sized enterprises (SMEs) despite their theoretical and practical significance. Drawing from strain perspective, this research intends to address this insufficiency and investigate SME owners’ ethical attitudes towards money-related deviances.
Design/methodology/approach: Based on a large sample of 741 Chinese SMEs, an OLS regression analysis was employed to test associated hypotheses. The robustness of results was additionally checked.
Findings: Results suggest that for stratification variables, education level is positively related to ethical attitudes, whereas household income level is surprisingly negatively associated with ethical attitudes; for materialism facets, success and happiness exert a negative impact on ethical attitudes as hypothesized, but centrality has no associated impact.
Research limitations/implications: This study has examined both structural and motivational sources of personal strains on the ethical attitude of SME owners, while the characteristics of these strains could be explored in the future studies.
Originality/value: This study advances and complements the dominant behavior approach that emphasizes cognitive and other psychological processes in explaining individual ethical attitudes. It is also seemingly the first study to examine the influence of three materialism facets on entrepreneurial ethical attitudes
Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
How can we better extract entities and relations from text? Using multimodal
extraction with images and text obtains more signals for entities and
relations, and aligns them through graphs or hierarchical fusion, aiding in
extraction. Despite attempts at various fusions, previous works have overlooked
many unlabeled image-caption pairs, such as NewsCLIPing. This paper proposes
innovative pre-training objectives for entity-object and relation-image
alignment, extracting objects from images and aligning them with entity and
relation prompts for soft pseudo-labels. These labels are used as
self-supervised signals for pre-training, enhancing the ability to extract
entities and relations. Experiments on three datasets show an average 3.41% F1
improvement over prior SOTA. Additionally, our method is orthogonal to previous
multimodal fusions, and using it on prior SOTA fusions further improves 5.47%
F1.Comment: Accepted to ACM Multimedia 202
Variational Relational Point Completion Network for Robust 3D Classification
Real-scanned point clouds are often incomplete due to viewpoint, occlusion,
and noise, which hampers 3D geometric modeling and perception. Existing point
cloud completion methods tend to generate global shape skeletons and hence lack
fine local details. Furthermore, they mostly learn a deterministic
partial-to-complete mapping, but overlook structural relations in man-made
objects. To tackle these challenges, this paper proposes a variational
framework, Variational Relational point Completion Network (VRCNet) with two
appealing properties: 1) Probabilistic Modeling. In particular, we propose a
dual-path architecture to enable principled probabilistic modeling across
partial and complete clouds. One path consumes complete point clouds for
reconstruction by learning a point VAE. The other path generates complete
shapes for partial point clouds, whose embedded distribution is guided by
distribution obtained from the reconstruction path during training. 2)
Relational Enhancement. Specifically, we carefully design point self-attention
kernel and point selective kernel module to exploit relational point features,
which refines local shape details conditioned on the coarse completion. In
addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40
dataset) containing over 200,000 high-quality scans, which render partial 3D
shapes from 26 uniformly distributed camera poses for each 3D CAD model.
Extensive experiments demonstrate that VRCNet outperforms state-of-the-art
methods on all standard point cloud completion benchmarks. Notably, VRCNet
shows great generalizability and robustness on real-world point cloud scans.
Moreover, we can achieve robust 3D classification for partial point clouds with
the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage:
https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap
with arXiv:2104.1015
IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network
Infrared and visible image fusion (IVIF) is used to generate fusion images
with comprehensive features of both images, which is beneficial for downstream
vision tasks. However, current methods rarely consider the illumination
condition in low-light environments, and the targets in the fused images are
often not prominent. To address the above issues, we propose an
Illumination-Aware Infrared and Visible Image Fusion Network, named as IAIFNet.
In our framework, an illumination enhancement network first estimates the
incident illumination maps of input images. Afterwards, with the help of
proposed adaptive differential fusion module (ADFM) and salient target aware
module (STAM), an image fusion network effectively integrates the salient
features of the illumination-enhanced infrared and visible images into a fusion
image of high visual quality. Extensive experimental results verify that our
method outperforms five state-of-the-art methods of fusing infrared and visible
images.Comment: Submitted to IEE
SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion
Most existing learning-based infrared and visible image fusion (IVIF) methods
exhibit massive redundant information in the fusion images, i.e., yielding
edge-blurring effect or unrecognizable for object detectors. To alleviate these
issues, we propose a semantic structure-preserving approach for IVIF, namely
SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract
the structural features of infrared and visible images. Then, we introduce a
multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural
features of infrared and visible images, while maintaining the consistency of
semantic structures between the fusion and source images. Owing to these two
effective modules, our method is able to generate high-quality fusion images
from pairs of infrared and visible images, which can boost the performance of
downstream computer-vision tasks. Experimental results on three benchmarks
demonstrate that our method outperforms eight state-of-the-art image fusion
methods in terms of both qualitative and quantitative evaluations. The code for
our method, along with additional comparison results, will be made available
at: https://github.com/QiaoYang-CV/SSPFUSION.Comment: Submitted to IEE
RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia
Background While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways.
Results We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis.
Conclusions Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia
Primary prevention for risk factors of ischemic stroke with Baduanjin exercise intervention in the community elder population: study protocol for a randomized controlled trial
BACKGROUND: Stroke is a major cause of death and disability in the world, and the prevalence of stroke tends to increase with age. Despite advances in acute care and secondary preventive strategies, primary prevention should play the most significant role in the reduction of the burden of stroke. As an important component of traditional Chinese Qigong, Baduanjin exercise is a simple, safe exercise, especially suitable for older adults. However, current evidence is insufficient to inform the use of Baduanjin exercise in the prevention of stroke. The aim of this trail is to systematically evaluate the prevention effect of Baduanjin exercise on ischemic stroke in the community elder population with high risk factors. METHODS: A total of 170 eligible participants from the community elder population will be randomly allocated into the Baduanjin exercise group and usual physical activity control group in a 1:1 ratio. Besides usual physical activity, participants in the Baduanjin exercise group will accept a 12-week Baduanjin exercise training with a frequency of five days a week and 40 minutes a day. Primary and secondary outcomes will be measured at baseline, 13 weeks (at end of intervention) and 25 weeks (after additional 12-week follow-up period). DISCUSSION: This study will be the randomized trial to evaluate the effectiveness of Baduanjin exercise for primary prevention of stroke in community elder population with high risk factors of stroke. The results of this trial will help to establish the optimal approach for primary prevention of stroke. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR-TRC-13003588. Registration date: 24 July, 2013
DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields
In this paper, we address the challenging problem of 3D toonification, which
involves transferring the style of an artistic domain onto a target 3D face
with stylized geometry and texture. Although fine-tuning a pre-trained 3D GAN
on the artistic domain can produce reasonable performance, this strategy has
limitations in the 3D domain. In particular, fine-tuning can deteriorate the
original GAN latent space, which affects subsequent semantic editing, and
requires independent optimization and storage for each new style, limiting
flexibility and efficient deployment. To overcome these challenges, we propose
DeformToon3D, an effective toonification framework tailored for hierarchical 3D
GAN. Our approach decomposes 3D toonification into subproblems of geometry and
texture stylization to better preserve the original latent space. Specifically,
we devise a novel StyleField that predicts conditional 3D deformation to align
a real-space NeRF to the style space for geometry stylization. Thanks to the
StyleField formulation, which already handles geometry stylization well,
texture stylization can be achieved conveniently via adaptive style mixing that
injects information of the artistic domain into the decoder of the pre-trained
3D GAN. Due to the unique design, our method enables flexible style degree
control and shape-texture-specific style swap. Furthermore, we achieve
efficient training without any real-world 2D-3D training pairs but proxy
samples synthesized from off-the-shelf 2D toonification models.Comment: ICCV 2023. Code: https://github.com/junzhezhang/DeformToon3D Project
page: https://www.mmlab-ntu.com/project/deformtoon3d
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