185 research outputs found
Modelling studies on biological tissue properties and mechanical responses under external stimuli
PhDBiological tissues maintain their homeostasis by remodelling under external mechanical stimuli. In order to understand the tissue remodelling process, it is important to characterize tissue properties before detailed mechanical responses can be investigated. This project aims to develop a computational modelling framework to characterise mechanical properties of biological tissues, and to quantify tissue responses under mechanical loading.
The thesis presents, first, mechanical responses of articular cartilages under different loadings using a poroelastic model. Unique in this study, collagen fibrils are treated separately from the rest of ECM, as they only resists tension. This leads to a fibril-reinforced poroelastic model. Effects of the distribution of the collagen fibrils and their orientation on tissue mechanical responses are investigated.
Most of the effort has been on the mechanical stress distribution of the human left atrium and its correlation to electrophysiology patterns in atrial fibrillation. Detailed mechanical responses of the atrial wall to a step pressure increase in the left atrium are calculated. The geometry of the left atrium is based on patient specific images using cardio CT and incorporates variations of the atrial wall thickness as well as unique fibre orientation patterns. We hypothesize that areas of high von Mises stress are correlated to foci of abnormal electrophysiology sites which sustain cardiac arrhythmia. Results from this study show a positive correlation between them. To our knowledge, this is the first study that establishes the relationship between the atrial wall stress distribution and the atrial abnormal electrophysiology sites.
The project also investigates hyperelastic properties of endothelial cells and the overlying endothelial glycocalyx, based on data from AFM micro-indentation. Both endothelial cells with & without the glycocalyx layer (i.e. following enzymatic digestion) are used. This is the first time that the mechanical property of the glycocalyx is estimated using an inverse biomechanical model
Modeling Relation Paths for Representation Learning of Knowledge Bases
Representation learning of knowledge bases (KBs) aims to embed both entities
and relations into a low-dimensional space. Most existing methods only consider
direct relations in representation learning. We argue that multiple-step
relation paths also contain rich inference patterns between entities, and
propose a path-based representation learning model. This model considers
relation paths as translations between entities for representation learning,
and addresses two key challenges: (1) Since not all relation paths are
reliable, we design a path-constraint resource allocation algorithm to measure
the reliability of relation paths. (2) We represent relation paths via semantic
composition of relation embeddings. Experimental results on real-world datasets
show that, as compared with baselines, our model achieves significant and
consistent improvements on knowledge base completion and relation extraction
from text.Comment: 10 page
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a
list of non-discrete attributes for each entity. Intuitively, these attributes
such as height, price or population count are able to richly characterize
entities in knowledge graphs. This additional source of information may help to
alleviate the inherent sparsity and incompleteness problem that are prevalent
in knowledge graphs. Unfortunately, many state-of-the-art relational learning
models ignore this information due to the challenging nature of dealing with
non-discrete data types in the inherently binary-natured knowledge graphs. In
this paper, we propose a novel multi-task neural network approach for both
encoding and prediction of non-discrete attribute information in a relational
setting. Specifically, we train a neural network for triplet prediction along
with a separate network for attribute value regression. Via multi-task
learning, we are able to learn representations of entities, relations and
attributes that encode information about both tasks. Moreover, such attributes
are not only central to many predictive tasks as an information source but also
as a prediction target. Therefore, models that are able to encode, incorporate
and predict such information in a relational learning context are highly
attractive as well. We show that our approach outperforms many state-of-the-art
methods for the tasks of relational triplet classification and attribute value
prediction.Comment: Accepted at CIKM 201
Model-free screening procedure for ultrahigh-dimensional survival data based on Hilbert-Schmidt independence criterion
How to select the active variables which have significant impact on the event
of interest is a very important and meaningful problem in the statistical
analysis of ultrahigh-dimensional data. Sure independent screening procedure
has been demonstrated to be an effective method to reduce the dimensionality of
data from a large scale to a relatively moderate scale. For censored survival
data, the existing screening methods mainly adopt the Kaplan--Meier estimator
to handle censoring, which may not perform well for scenarios which have heavy
censoring rate. In this article, we propose a model-free screening procedure
based on the Hilbert-Schmidt independence criterion (HSIC). The proposed method
avoids the complication to specify an actual model from a large number of
covariates. Compared with existing screening procedures, this new approach has
several advantages. First, it does not involve the Kaplan--Meier estimator,
thus its performance is much more robust for the cases with a heavy censoring
rate. Second, the empirical estimate of HSIC is very simple as it just depends
on the trace of a product of Gram matrices. In addition, the proposed procedure
does not require any complicated numerical optimization, so the corresponding
calculation is very simple and fast. Finally, the proposed procedure which
employs the kernel method is substantially more resistant to outliers.
Extensive simulation studies demonstrate that the proposed method has favorable
exhibition over the existing methods. As an illustration, we apply the proposed
method to analyze the diffuse large-B-cell lymphoma (DLBCL) data and the
ovarian cancer data
NumNet: Machine Reading Comprehension with Numerical Reasoning
Numerical reasoning, such as addition, subtraction, sorting and counting is a
critical skill in human's reading comprehension, which has not been well
considered in existing machine reading comprehension (MRC) systems. To address
this issue, we propose a numerical MRC model named as NumNet, which utilizes a
numerically-aware graph neural network to consider the comparing information
and performs numerical reasoning over numbers in the question and passage. Our
system achieves an EM-score of 64.56% on the DROP dataset, outperforming all
existing machine reading comprehension models by considering the numerical
relations among numbers.Comment: Accepted to EMNLP2019; 11 pages, 2 figures, 6 table
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