60 research outputs found
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Chest X-Ray (CXR) images are commonly used for clinical screening and
diagnosis. Automatically writing reports for these images can considerably
lighten the workload of radiologists for summarizing descriptive findings and
conclusive impressions. The complex structures between and within sections of
the reports pose a great challenge to the automatic report generation.
Specifically, the section Impression is a diagnostic summarization over the
section Findings; and the appearance of normality dominates each section over
that of abnormality. Existing studies rarely explore and consider this
fundamental structure information. In this work, we propose a novel framework
that exploits the structure information between and within report sections for
generating CXR imaging reports. First, we propose a two-stage strategy that
explicitly models the relationship between Findings and Impression. Second, we
design a novel cooperative multi-agent system that implicitly captures the
imbalanced distribution between abnormality and normality. Experiments on two
CXR report datasets show that our method achieves state-of-the-art performance
in terms of various evaluation metrics. Our results expose that the proposed
approach is able to generate high-quality medical reports through integrating
the structure information.Comment: ACL 201
COIN: Co-Cluster Infomax for Bipartite Graphs
Bipartite graphs are powerful data structures to model interactions between
two types of nodes, which have been used in a variety of applications, such as
recommender systems, information retrieval, and drug discovery. A fundamental
challenge for bipartite graphs is how to learn informative node embeddings.
Despite the success of recent self-supervised learning methods on bipartite
graphs, their objectives are discriminating instance-wise positive and negative
node pairs, which could contain cluster-level errors. In this paper, we
introduce a novel co-cluster infomax (COIN) framework, which captures the
cluster-level information by maximizing the mutual information of co-clusters.
Different from previous infomax methods which estimate mutual information by
neural networks, COIN could easily calculate mutual information. Besides, COIN
is an end-to-end coclustering method which can be trained jointly with other
objective functions and optimized via back-propagation. Furthermore, we also
provide theoretical analysis for COIN. We theoretically prove that COIN is able
to effectively increase the mutual information of node embeddings and COIN is
upper-bounded by the prior distributions of nodes. We extensively evaluate the
proposed COIN framework on various benchmark datasets and tasks to demonstrate
the effectiveness of COIN.Comment: NeurIPS 2022 GLFrontiers Worksho
laboratory trials and design of industrial application of hot stamping of 22mnb5 tailored components by partition heating
Abstract Hot stamping of 22MnB5 by partition heating may represent an effective method to produce tailored parts of the car body-in-white to improve collision performances. This paper investigated its feasibility and applicability at both laboratory and industrial level. First, an M-shaped part was stamped using a specially-designed partition heating device, and its finite element model established and validated. Secondly, a partition heating device suitable for industry was designed for stamping tailored B-pillars. The hot stamping by partition heating to produce B-pillars was simulated, analysing major characteristics during and after stamping and quenching
- …