69 research outputs found
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Incomplete multi-view clustering (IMVC) has received increasing attention
since it is often that some views of samples are incomplete in reality. Most
existing methods learn similarity subgraphs from original incomplete multi-view
data and seek complete graphs by exploring the incomplete subgraphs of each
view for spectral clustering. However, the graphs constructed on the original
high-dimensional data may be suboptimal due to feature redundancy and noise.
Besides, previous methods generally ignored the graph noise caused by the
inter-class and intra-class structure variation during the transformation of
incomplete graphs and complete graphs. To address these problems, we propose a
novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD)
for IMVC. Specifically, to alleviate the influence of redundant features and
noise in high-dimensional data, JPLTD introduces an orthogonal projection
matrix to project the high-dimensional features into a lower-dimensional space
for compact feature learning.Meanwhile, based on the lower-dimensional space,
the similarity graphs corresponding to instances of different views are
learned, and JPLTD stacks these graphs into a third-order low-rank tensor to
explore the high-order correlations across different views. We further consider
the graph noise of projected data caused by missing samples and use a
tensor-decomposition based graph filter for robust clustering.JPLTD decomposes
the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic
tensor models the true data similarities. An effective optimization algorithm
is adopted to solve the JPLTD model. Comprehensive experiments on several
benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art
methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Extensive experimental results demonstrate that our model
achieves new State-Of-The-Art (SOTA) performance and outperforms baselines
remarkably
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
Incremental Named Entity Recognition (INER) involves the sequential learning
of new entity types without accessing the training data of previously learned
types. However, INER faces the challenge of catastrophic forgetting specific
for incremental learning, further aggravated by background shift (i.e., old and
future entity types are labeled as the non-entity type in the current task). To
address these challenges, we propose a method called task Relation Distillation
and Prototypical pseudo label (RDP) for INER. Specifically, to tackle
catastrophic forgetting, we introduce a task relation distillation scheme that
serves two purposes: 1) ensuring inter-task semantic consistency across
different incremental learning tasks by minimizing inter-task relation
distillation loss, and 2) enhancing the model's prediction confidence by
minimizing intra-task self-entropy loss. Simultaneously, to mitigate background
shift, we develop a prototypical pseudo label strategy that distinguishes old
entity types from the current non-entity type using the old model. This
strategy generates high-quality pseudo labels by measuring the distances
between token embeddings and type-wise prototypes. We conducted extensive
experiments on ten INER settings of three benchmark datasets (i.e., CoNLL2003,
I2B2, and OntoNotes5). The results demonstrate that our method achieves
significant improvements over the previous state-of-the-art methods, with an
average increase of 6.08% in Micro F1 score and 7.71% in Macro F1 score.Comment: Accepted by CIKM2023 as a long paper with an oral presentatio
Continual Named Entity Recognition without Catastrophic Forgetting
Continual Named Entity Recognition (CNER) is a burgeoning area, which
involves updating an existing model by incorporating new entity types
sequentially. Nevertheless, continual learning approaches are often severely
afflicted by catastrophic forgetting. This issue is intensified in CNER due to
the consolidation of old entity types from previous steps into the non-entity
type at each step, leading to what is known as the semantic shift problem of
the non-entity type. In this paper, we introduce a pooled feature distillation
loss that skillfully navigates the trade-off between retaining knowledge of old
entity types and acquiring new ones, thereby more effectively mitigating the
problem of catastrophic forgetting. Additionally, we develop a confidence-based
pseudo-labeling for the non-entity type, \emph{i.e.,} predicting entity types
using the old model to handle the semantic shift of the non-entity type.
Following the pseudo-labeling process, we suggest an adaptive re-weighting
type-balanced learning strategy to handle the issue of biased type
distribution. We carried out comprehensive experiments on ten CNER settings
using three different datasets. The results illustrate that our method
significantly outperforms prior state-of-the-art approaches, registering an
average improvement of \% and \% in Micro and Macro F1 scores,
respectively.Comment: Accepted by EMNLP2023 main conference as a long pape
Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning
ChatGPT, launched in November 2022, has gained widespread attention from
students and educators globally, with an online report by Hu (2023) stating it
as the fastest-growing consumer application in history. While discussions on
the use of ChatGPT in higher education are abundant, empirical studies on its
impact on collaborative interdisciplinary learning are rare. To investigate its
potential, we conducted a quasi-experimental study with 130 undergraduate
students (STEM and non-STEM) learning digital literacy with or without ChatGPT
over two weeks. Weekly surveys were conducted on collaborative
interdisciplinary problem-solving, physical and cognitive engagement, and
individual reflections on ChatGPT use. Analysis of survey responses showed
significant main effects of topics on collaborative interdisciplinary
problem-solving and physical and cognitive engagement, a marginal interaction
effect between disciplinary backgrounds and ChatGPT conditions for cognitive
engagement, and a significant interaction effect for physical engagement.
Sentiment analysis of student reflections suggested no significant difference
between STEM and non-STEM students' opinions towards ChatGPT. Qualitative
analysis of reflections generated eight positive themes, including efficiency,
addressing knowledge gaps, and generating human-like responses, and eight
negative themes, including generic responses, lack of innovation, and
counterproductive to self-discipline and thinking. Our findings suggest that
ChatGPT use needs to be optimized by considering the topics being taught and
the disciplinary backgrounds of students rather than applying it uniformly.
These findings have implications for both pedagogical research and practices.Comment: 33 pages, 2 figures, 5 table
The downstream of tyrosine kinase 7 is reduced in lung cancer and is associated with poor survival of patients with lung cancer
The downstream of tyrosine kinase 7 (DOK7) is an adaptor protein mediating signalling transduction between receptors and intracellular downstream molecules. Reduced expression of DOK7 has been observed in breast cancer. The present study aimed to investigate the role played by DOK7 in lung cancer. The expression of DOK7 at both mRNA and protein levels was evaluated in human lung cancer. A reduced expression of DOK7 transcripts was seen in lung cancers compared with normal lung tissues. Kaplan-Meier analyses showed that the reduced expression of DOK7 was associated with poorer overall survival and progression-free survival of patients with lung cancer. A further western blot analysis revealed a predominant expression of DOK7 isoform 1 (DOK7V1) in normal lung tissues, which was reduced in lung cancer. Forced overexpression of DOK7V1 in lung cancer cell lines, A549 and H3122 resulted in a decrease of in vitro cell proliferation and migration, while adhesion to extracellular matrix was enhanced following the expression. In conclusion, DOK7 was reduced in lung cancer and reduced DOK7 expression was associated with poorer survival. DOK7 isoform 1 plays an inhibitory role on the proliferation and migration of lung cancer cells in which Akt pathway may be involved
Integrated Project and Supply Chain Management in Well Drilling Process
This thesis work provides a mixed integer programming model to help integrating the drilling operation and supplier selection in well drilling process of oil/gas production. The appropriate decisions on the services orders are taken based on three criteria including service duration, cost and timely deliverance. The schedule of drilling operation is based on regular working time and overtime. The research outcomes provide the optimal or rational solutions for the decisions on: supplier selection, regular working time vs. overtime planning for each activity, and the total project duration with the minimum total project cost. The two typical drilling operation project cases from a local oil/gas company are collected and conducted to validate the feasibility and effectiveness of the model. In the thesis, the conflicts and trade-offs on the business profits and project time control between the operator company and its suppliers are also discussed. To solve the problem resulted from divergent positions between the operator company and its suppliers, the sharing risk and incentive contract are suggested to be adopted by the operator companies in oil/gas production from other manufacturing research and applications. In short, this study is novel and beneficial for drilling project management as it could improve the performance of drilling operations and to integrate the activities of the suppliers
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