2,098 research outputs found
Strain-engineered A-type antiferromagnetic order in YTiO: a first-principles calculation
The epitaxial strain effects on the magnetic ground state of YTiO films
grown on LaAlO substrates have been studied using the first-principles
density-functional theory. With the in-plane compressive strain induced by
LaAlO (001) substrate, A-type antiferromagnetic order emerges against the
original ferromagnetic order. This phase transition from ferromagnet to A-type
antiferromagnet in YTiO film is robust since the energy gain is about 7.64
meV per formula unit despite the Hubbard interaction and modest lattice
changes, even though the A-type antiferromagnetic order does not exist in any
TiO bulks.Comment: 3 pages, 2 figures. Proceeding of the 12th Joint MMM/Intermag
Conference. Accepted by JA
A Multi-Objective DIRECT Algorithm Towards Structural Damage Identification with Limited Dynamic Response Information
A major challenge in Structural Health Monitoring (SHM) is to accurately
identify both the location and severity of damage using the dynamic response
information acquired. While in theory the vibration-based and impedance-based
methods may facilitate damage identification with the assistance of a credible
baseline finite element model since the changes of stationary wave responses
are used in these methods, the response information is generally limited and
the measurements may be heterogeneous, making an inverse analysis using
sensitivity matrix difficult. Aiming at fundamental advancement, in this
research we cast the damage identification problem into an optimization problem
where possible changes of finite element properties due to damage occurrence
are treated as unknowns. We employ the multiple damage location assurance
criterion (MDLAC), which characterizes the relation between measurements and
predictions (under sampled elemental property changes), as the vector-form
objective function. We then develop an enhanced, multi-objective version of the
DIRECT approach to solve the optimization problem. The underlying idea of the
multi-objective DIRECT approach is to branch and bound the unknown parametric
space to converge to a set of optimal solutions. A new sampling scheme is
established, which significantly increases the efficiency in minimizing the
error between measurements and predictions. The enhanced DIRECT algorithm is
particularly suitable to solving for unknowns that are sparse, as in practical
situations structural damage affect only a small number of finite elements. A
number of test cases using vibration response information are executed to
demonstrate the effectiveness of the new approach
Improving Sentence Representations with Consensus Maximisation
Consensus maximisation learning can provide self-supervision when different
views are available of the same data. The distributional hypothesis provides
another form of useful self-supervision from adjacent sentences which are
plentiful in large unlabelled corpora. Motivated by the observation that
different learning architectures tend to emphasise different aspects of
sentence meaning, we present a new self-supervised learning framework for
learning sentence representations which minimises the disagreement between two
views of the same sentence where one view encodes the sentence with a recurrent
neural network (RNN), and the other view encodes the same sentence with a
simple linear model. After learning, the individual views (networks) result in
higher quality sentence representations than their single-view learnt
counterparts (learnt using only the distributional hypothesis) as judged by
performance on standard downstream tasks. An ensemble of both views provides
even better generalisation on both supervised and unsupervised downstream
tasks. Also, importantly the ensemble of views trained with consensus
maximisation between the two different architectures performs better on
downstream tasks than an analogous ensemble made from the single-view trained
counterparts.Comment: arXiv admin note: substantial text overlap with arXiv:1805.0744
Multi-view Sentence Representation Learning
Multi-view learning can provide self-supervision when different views are
available of the same data. The distributional hypothesis provides another form
of useful self-supervision from adjacent sentences which are plentiful in large
unlabelled corpora. Motivated by the asymmetry in the two hemispheres of the
human brain as well as the observation that different learning architectures
tend to emphasise different aspects of sentence meaning, we create a unified
multi-view sentence representation learning framework, in which, one view
encodes the input sentence with a Recurrent Neural Network (RNN), and the other
view encodes it with a simple linear model, and the training objective is to
maximise the agreement specified by the adjacent context information between
two views. We show that, after training, the vectors produced from our
multi-view training provide improved representations over the single-view
training, and the combination of different views gives further representational
improvement and demonstrates solid transferability on standard downstream
tasks
Leveraging Gaussian Process and Voting-Empowered Many-Objective Evaluation for Fault Identification
Using piezoelectric impedance/admittance sensing for structural health
monitoring is promising, owing to the simplicity in circuitry design as well as
the high-frequency interrogation capability. The actual identification of fault
location and severity using impedance/admittance measurements, nevertheless,
remains to be an extremely challenging task. A first-principle based structural
model using finite element discretization requires high dimensionality to
characterize the high-frequency response. As such, direct inversion using the
sensitivity matrix usually yields an under-determined problem. Alternatively,
the identification problem may be cast into an optimization framework in which
fault parameters are identified through repeated forward finite element
analysis which however is oftentimes computationally prohibitive. This paper
presents an efficient data-assisted optimization approach for fault
identification without using finite element model iteratively. We formulate a
many-objective optimization problem to identify fault parameters, where
response surfaces of impedance measurements are constructed through Gaussian
process-based calibration. To balance between solution diversity and
convergence, an -dominance enabled many-objective simulated annealing algorithm
is established. As multiple solutions are expected, a voting score calculation
procedure is developed to further identify those solutions that yield better
implications regarding structural health condition. The effectiveness of the
proposed approach is demonstrated by systematic numerical and experimental case
studies
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
The encoder-decoder models for unsupervised sentence representation learning
tend to discard the decoder after being trained on a large unlabelled corpus,
since only the encoder is needed to map the input sentence into a vector
representation. However, parameters learnt in the decoder also contain useful
information about language. In order to utilise the decoder after learning, we
present two types of decoding functions whose inverse can be easily derived
without expensive inverse calculation. Therefore, the inverse of the decoding
function serves as another encoder that produces sentence representations. We
show that, with careful design of the decoding functions, the model learns good
sentence representations, and the ensemble of the representations produced from
the encoder and the inverse of the decoder demonstrate even better
generalisation ability and solid transferability
What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks
Top-down information plays a central role in human perception, but plays
relatively little role in many current state-of-the-art deep networks, such as
Convolutional Neural Networks (CNNs). This work seeks to explore a path by
which top-down information can have a direct impact within current deep
networks. We explore this path by learning and using "generators" corresponding
to the network internal effects of three types of transformation (each a
restriction of a general affine transformation): rotation, scaling, and
translation. We demonstrate how these learned generators can be used to
transfer top-down information to novel settings, as mediated by the "feature
flows" that the transformations (and the associated generators) correspond to
inside the network. Specifically, we explore three aspects: 1) using generators
as part of a method for synthesizing transformed images --- given a previously
unseen image, produce versions of that image corresponding to one or more
specified transformations, 2) "zero-shot learning" --- when provided with a
feature flow corresponding to the effect of a transformation of unknown amount,
leverage learned generators as part of a method by which to perform an accurate
categorization of the amount of transformation, even for amounts never observed
during training, and 3) (inside-CNN) "data augmentation" --- improve the
classification performance of an existing network by using the learned
generators to directly provide additional training "inside the CNN"
A Simple Recurrent Unit with Reduced Tensor Product Representations
idely used recurrent units, including Long-short Term Memory (LSTM) and the
Gated Recurrent Unit (GRU), perform well on natural language tasks, but their
ability to learn structured representations is still questionable. Exploiting
reduced Tensor Product Representations (TPRs) --- distributed representations
of symbolic structure in which vector-embedded symbols are bound to
vector-embedded structural positions --- we propose the TPRU, a simple
recurrent unit that, at each time step, explicitly executes structural-role
binding and unbinding operations to incorporate structural information into
learning. A gradient analysis of our proposed TPRU is conducted to support our
model design, and its performance on multiple datasets shows the effectiveness
of our design choices. Furthermore, observations on a linguistically grounded
study demonstrate the interpretability of our TPRU
Hierarchical Deep Recurrent Architecture for Video Understanding
This paper introduces the system we developed for the Youtube-8M Video
Understanding Challenge, in which a large-scale benchmark dataset was used for
multi-label video classification. The proposed framework contains hierarchical
deep architecture, including the frame-level sequence modeling part and the
video-level classification part. In the frame-level sequence modelling part, we
explore a set of methods including Pooling-LSTM (PLSTM), Hierarchical-LSTM
(HLSTM), Random-LSTM (RLSTM) in order to address the problem of large amount of
frames in a video. We also introduce two attention pooling methods, single
attention pooling (ATT) and multiply attention pooling (Multi-ATT) so that we
can pay more attention to the informative frames in a video and ignore the
useless frames. In the video-level classification part, two methods are
proposed to increase the classification performance, i.e.
Hierarchical-Mixture-of-Experts (HMoE) and Classifier Chains (CC). Our final
submission is an ensemble consisting of 18 sub-models. In terms of the official
evaluation metric Global Average Precision (GAP) at 20, our best submission
achieves 0.84346 on the public 50% of test dataset and 0.84333 on the private
50% of test data.Comment: Accepted as Classification Challenge Track paper in CVPR 2017
Workshop on YouTube-8M Large-Scale Video Understandin
An Empirical Study on Post-processing Methods for Word Embeddings
Word embeddings learnt from large corpora have been adopted in various
applications in natural language processing and served as the general input
representations to learning systems. Recently, a series of post-processing
methods have been proposed to boost the performance of word embeddings on
similarity comparison and analogy retrieval tasks, and some have been adapted
to compose sentence representations. The general hypothesis behind these
methods is that by enforcing the embedding space to be more isotropic, the
similarity between words can be better expressed. We view these methods as an
approach to shrink the covariance/gram matrix, which is estimated by learning
word vectors, towards a scaled identity matrix. By optimising an objective in
the semi-Riemannian manifold with Centralised Kernel Alignment (CKA), we are
able to search for the optimal shrinkage parameter, and provide a
post-processing method to smooth the spectrum of learnt word vectors which
yields improved performance on downstream tasks
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