59 research outputs found
Efficient Parallel Translating Embedding For Knowledge Graphs
Knowledge graph embedding aims to embed entities and relations of knowledge
graphs into low-dimensional vector spaces. Translating embedding methods regard
relations as the translation from head entities to tail entities, which achieve
the state-of-the-art results among knowledge graph embedding methods. However,
a major limitation of these methods is the time consuming training process,
which may take several days or even weeks for large knowledge graphs, and
result in great difficulty in practical applications. In this paper, we propose
an efficient parallel framework for translating embedding methods, called
ParTrans-X, which enables the methods to be paralleled without locks by
utilizing the distinguished structures of knowledge graphs. Experiments on two
datasets with three typical translating embedding methods, i.e., TransE [3],
TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that
ParTrans-X can speed up the training process by more than an order of
magnitude.Comment: WI 2017: 460-46
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Long-tailed relation classification is a challenging problem as the head
classes may dominate the training phase, thereby leading to the deterioration
of the tail performance. Existing solutions usually address this issue via
class-balancing strategies, e.g., data re-sampling and loss re-weighting, but
all these methods adhere to the schema of entangling learning of the
representation and classifier. In this study, we conduct an in-depth empirical
investigation into the long-tailed problem and found that pre-trained models
with instance-balanced sampling already capture the well-learned
representations for all classes; moreover, it is possible to achieve better
long-tailed classification ability at low cost by only adjusting the
classifier. Inspired by this observation, we propose a robust classifier with
attentive relation routing, which assigns soft weights by automatically
aggregating the relations. Extensive experiments on two datasets demonstrate
the effectiveness of our proposed approach. Code and datasets are available in
https://github.com/zjunlp/deepke
Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model
Entity extraction and relation extraction are two indispensable building
blocks for knowledge graph construction. Recent works on entity and relation
extraction have shown the superiority of solving the two problems in a joint
manner, where entities and relations are extracted simultaneously to form
relational triples in a knowledge graph. However, existing methods ignore the
hierarchical semantic interdependency between entity extraction (EE) and joint
extraction (JE), which leaves much to be desired in real applications. In this
work, we propose a hierarchical multi-task tagging model, called HMT, which
captures such interdependency and achieves better performance for joint
extraction of entities and relations. Specifically, the EE task is organized at
the bottom layer and JE task at the top layer in a hierarchical structure.
Furthermore, the learned semantic representation at the lower level can be
shared by the upper level via multi-task learning. Experimental results
demonstrate the effectiveness of the proposed model for joint extraction in
comparison with the state-of-the-art methods.Comment: 10 pages, 3 figure
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Neural networks are known to be vulnerable to carefully crafted adversarial
examples, and these malicious samples often transfer, i.e., they remain
adversarial even against other models. Although great efforts have been delved
into the transferability across models, surprisingly, less attention has been
paid to the cross-task transferability, which represents the real-world
cybercriminal's situation, where an ensemble of different defense/detection
mechanisms need to be evaded all at once. In this paper, we investigate the
transferability of adversarial examples across a wide range of real-world
computer vision tasks, including image classification, object detection,
semantic segmentation, explicit content detection, and text detection. Our
proposed attack minimizes the ``dispersion'' of the internal feature map, which
overcomes existing attacks' limitation of requiring task-specific loss
functions and/or probing a target model. We conduct evaluation on open source
detection and segmentation models as well as four different computer vision
tasks provided by Google Cloud Vision (GCV) APIs, to show how our approach
outperforms existing attacks by degrading performance of multiple CV tasks by a
large margin with only modest perturbations linf=16.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0333
Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells
BackgroundMelanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity.Materials and methodsCells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell–cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines.ResultsFollowing batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells’ proliferation, invasion, and migration.ConclusionThe differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells
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