4,611 research outputs found
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Transport properties in Simplified Double Exchange model
Transport properties of the manganites by the double-exchange mechanism are
considered. The system is modeled by a simplified double-exchange model, i.e.
the Hund coupling of the itinerant electron spins and local spins is simplified
to the Ising-type one. The transport properties such as the electronic
resistivity, the thermal conductivity, and the thermal power are calculated by
using Dynamical mean-field theory. The transport quantities obtained
qualitatively reproduce the ones observed in the manganites. The results
suggest that the Simplified double exchange model underlies the key properties
of the manganites.Comment: 5 pages, 5 eps figure
Enriching Biomedical Knowledge for Vietnamese Low-resource Language Through Large-Scale Translation
Biomedical data and benchmarks are highly valuable yet very limited in
low-resource languages other than English such as Vietnamese. In this paper, we
make use of a state-of-the-art translation model in English-Vietnamese to
translate and produce both pretrained as well as supervised data in the
biomedical domains. Thanks to such large-scale translation, we introduce
ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20
million translated abstracts from the high-quality public PubMed corpus.
ViPubMedT5 demonstrates state-of-the-art results on two different biomedical
benchmarks in summarization and acronym disambiguation. Further, we release
ViMedNLI - a new NLP task in Vietnamese translated from MedNLI using the
recently public En-vi translation model and carefully refined by human experts,
with evaluations of existing methods against ViPubmedT5
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