54 research outputs found
Resilience As A Mediator Between Social Support And Mental Health Among Children Affected By Hiv/aids In China
RESILIENCE AS A MEDIATOR BETWEEN SOCIAL SUPPORT AND MENTAL HEALTH AMONG CHILDREN AFFECTED BY HIV/AIDS IN CHINA
by
CHENGUANG DU
THESIS
Submitted to the Graduate School
of Wayne State University,
Detroit, Michigan
in partial fulfillment of the requirements
for the degree of
MASTER OF EDUCATION
2016
MAJOR: EDUCATION EVALUATION and RESEARCH
Approved By:
Advisor Dat
Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling
Understanding determinants of success in academic careers is critically
important to both scholars and their employing organizations. While
considerable research efforts have been made in this direction, there is still
a lack of a quantitative approach to modeling the academic careers of scholars
due to the massive confounding factors. To this end, in this paper, we propose
to explore the determinants of academic career success through an empirical and
predictive modeling perspective, with a focus on two typical academic honors,
i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different
factors quantitatively, and obtain some insightful findings. Specifically, we
analyze the co-author network and find that potential scholars work closely
with influential scholars early on and more closely as they grow. Then we
compare the academic performance of male and female Fellows. After comparison,
we find that to be elected, females need to put in more effort than males. In
addition, we also find that being a Fellow could not bring the improvements of
citations and productivity growth. We hope these derived factors and findings
can help scholars to improve their competitiveness and develop well in their
academic careers
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
Recent years have witnessed the rapid development of heterogeneous graph
neural networks (HGNNs) in information retrieval (IR) applications. Many
existing HGNNs design a variety of tailor-made graph convolutions to capture
structural and semantic information in heterogeneous graphs. However, existing
HGNNs usually represent each node as a single vector in the multi-layer graph
convolution calculation, which makes the high-level graph convolution layer
fail to distinguish information from different relations and different orders,
resulting in the information loss in the message passing. %insufficient mining
of information. To this end, we propose a novel heterogeneous graph neural
network with sequential node representation, namely Seq-HGNN. To avoid the
information loss caused by the single vector node representation, we first
design a sequential node representation learning mechanism to represent each
node as a sequence of meta-path representations during the node message
passing. Then we propose a heterogeneous representation fusion module,
empowering Seq-HGNN to identify important meta-paths and aggregate their
representations into a compact one. We conduct extensive experiments on four
widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph
Benchmark (OGB). Experimental results show that our proposed method outperforms
state-of-the-art baselines in both accuracy and efficiency. The source code is
available at https://github.com/nobrowning/SEQ_HGNN.Comment: SIGIR 202
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
Heterogeneous graph neural networks (HGNNs) have been widely applied in
heterogeneous information network tasks, while most HGNNs suffer from poor
scalability or weak representation when they are applied to large-scale
heterogeneous graphs. To address these problems, we propose a novel
Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning
(RHCO) for large-scale heterogeneous graph representation learning. Unlike
traditional heterogeneous graph neural networks, we adopt the contrastive
learning mechanism to deal with the complex heterogeneity of large-scale
heterogeneous graphs. We first learn relation-aware node embeddings under the
network schema view. Then we propose a novel positive sample selection strategy
to choose meaningful positive samples. After learning node embeddings under the
positive sample graph view, we perform a cross-view contrastive learning to
obtain the final node representations. Moreover, we adopt the label smoothing
technique to boost the performance of RHCO. Extensive experiments on three
large-scale academic heterogeneous graph datasets show that RHCO achieves best
performance over the state-of-the-art models
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
Accurate citation count prediction of newly published papers could help
editors and readers rapidly figure out the influential papers in the future.
Though many approaches are proposed to predict a paper's future citation, most
ignore the dynamic heterogeneous graph structure or node importance in academic
networks. To cope with this problem, we propose a Dynamic heterogeneous Graph
and Node Importance network (DGNI) learning framework, which fully leverages
the dynamic heterogeneous graph and node importance information to predict
future citation trends of newly published papers. First, a dynamic
heterogeneous network embedding module is provided to capture the dynamic
evolutionary trends of the whole academic network. Then, a node importance
embedding module is proposed to capture the global consistency relationship to
figure out each paper's node importance. Finally, the dynamic evolutionary
trend embeddings and node importance embeddings calculated above are combined
to jointly predict the future citation counts of each paper, by a log-normal
distribution model according to multi-faced paper node representations.
Extensive experiments on two large-scale datasets demonstrate that our model
significantly improves all indicators compared to the SOTA models.Comment: Accepted by CIKM'202
DICER1 regulated let-7 expression levels in p53-induced cancer repression requires cyclin D1.
Let-7 miRNAs act as tumour suppressors by directly binding to the 3\u27UTRs of downstream gene products. The regulatory role of let-7 in downstream gene expression has gained much interest in the cancer research community, as it controls multiple biological functions and determines cell fates. For example, one target of the let-7 family is cyclin D1, which promotes G0/S cell cycle progression and oncogenesis, was correlated with endoribonuclease DICER1, another target of let-7. Down-regulated let-7 has been identified in many types of tumours, suggesting a feedback loop may exist between let-7 and cyclin D1. A potential player in the proposed feedback relationship is Dicer, a central regulator of miRNA expression through sequence-specific silencing. We first identified that DICER1 is the key downstream gene for cyclin D1-induced let-7 expression. In addition, we found that let-7 miRNAs expression decreased because of the p53-induced cell death response, with deregulated cyclin D1. Our results also showed that cyclin D1 is required for Nutlin-3 and TAX-induced let-7 expression in cancer repression and the cell death response. For the first time, we provide evidence that let-7 and cyclin D1 form a feedback loop in regulating therapy response of cancer cells and cancer stem cells, and importantly, that alteration of let-7 expression, mainly caused by cyclin D1, is a sensitive indicator for better chemotherapies response
Reconstruction of bone defect with autograft fibula and retained part of tibia after marginal resection of periosteal osteosarcoma: a case report
A Quantum-Inspired Direct Learning Strategy for Positive and Unlabeled Data
Abstract Learning from only positive and unlabeled (PU) data has broad applications in fields such as web data mining, product recommendations and medical diagnosis, which aims to train a binary classifier in the absence of negative labeled data. However, due to the lack of negative label information, prevailing PU learning methods usually rely on prior knowledge of unknown class distributions heavily. In fact, without additional constraints imposed by the prior knowledge, a direct learning strategy to coordinate the underlying clustering information in unlabeled data with the label information from positive training data is often considered challenging. To tackle this challenge, we propose a direct PU learning strategy using quantum formalization. By employing neural networks as backends, the samples are mapped into two-qubit composite systems, which should be understood here as mathematical entities encapsulating various classical distributions of two classical bits. Subsequently, the two qubits within the systems are trained to be as independent as possible from each other, capturing patterns of different classes. At the same time, their measurement results serving as the model outputs are encouraged to be maximally dissimilar. These characteristics enable effective training of classifiers on PU data. After formulating an appropriate discriminant rule, we introduce a quantum-inspired PU method named qPU using the direct learning strategy. This method not only has the potential to alleviate parameter sensitivity issues caused by prior estimation in other methods but is also straightforward to implement. Finally, experiments conducted on 13 classical datasets validate the effectiveness of qPU
Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events
The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L
A study of the collapse speed of bubble clusters
The collapse process of bubble cluster is closely related to bubble-bubble interaction. Theoretical analysis and numerical simulation are adopted to study the collapse of bubble cluster with various distributions. The key parameters for bubble collapse, including bubble quantity, volume fraction, and dimensionless pressure, are acquired by dimensional analysis. The effects of key parameters on collapse of bubble cluster are investigated by direct numerical simulation. The numerical result shows that the collapsing speed of bubble cluster increases with the increase of bubble quantity and dimensionless pressure, decreases with the increase of volume fraction. A condensation rate is considered on the basis of bubble cluster with primitive cubic distributions. Square pyramid arrangement and random arrangement of bubbles are also simulated. A parameter study of the dimensionless bubble distance bubble cluster with random arrangement shows that a larger distance generally results in a larger collapse speed of bubble cluster. (C) 2020 Elsevier Ltd. All rights reserved
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