4,511 research outputs found
Absolutely Maximally Entangled states, combinatorial designs and multi-unitary matrices
Absolutely Maximally Entangled (AME) states are those multipartite quantum
states that carry absolute maximum entanglement in all possible partitions. AME
states are known to play a relevant role in multipartite teleportation, in
quantum secret sharing and they provide the basis novel tensor networks related
to holography. We present alternative constructions of AME states and show
their link with combinatorial designs. We also analyze a key property of AME,
namely their relation to tensors that can be understood as unitary
transformations in every of its bi-partitions. We call this property
multi-unitarity.Comment: 18 pages, 2 figures. Comments are very welcom
Mixing times of lozenge tiling and card shuffling Markov chains
We show how to combine Fourier analysis with coupling arguments to bound the
mixing times of a variety of Markov chains. The mixing time is the number of
steps a Markov chain takes to approach its equilibrium distribution. One
application is to a class of Markov chains introduced by Luby, Randall, and
Sinclair to generate random tilings of regions by lozenges. For an L X L region
we bound the mixing time by O(L^4 log L), which improves on the previous bound
of O(L^7), and we show the new bound to be essentially tight. In another
application we resolve a few questions raised by Diaconis and Saloff-Coste, by
lower bounding the mixing time of various card-shuffling Markov chains. Our
lower bounds are within a constant factor of their upper bounds. When we use
our methods to modify a path-coupling analysis of Bubley and Dyer, we obtain an
O(n^3 log n) upper bound on the mixing time of the Karzanov-Khachiyan Markov
chain for linear extensions.Comment: 39 pages, 8 figure
Condition number analysis and preconditioning of the finite cell method
The (Isogeometric) Finite Cell Method - in which a domain is immersed in a
structured background mesh - suffers from conditioning problems when cells with
small volume fractions occur. In this contribution, we establish a rigorous
scaling relation between the condition number of (I)FCM system matrices and the
smallest cell volume fraction. Ill-conditioning stems either from basis
functions being small on cells with small volume fractions, or from basis
functions being nearly linearly dependent on such cells. Based on these two
sources of ill-conditioning, an algebraic preconditioning technique is
developed, which is referred to as Symmetric Incomplete Permuted Inverse
Cholesky (SIPIC). A detailed numerical investigation of the effectivity of the
SIPIC preconditioner in improving (I)FCM condition numbers and in improving the
convergence speed and accuracy of iterative solvers is presented for the
Poisson problem and for two- and three-dimensional problems in linear
elasticity, in which Nitche's method is applied in either the normal or
tangential direction. The accuracy of the preconditioned iterative solver
enables mesh convergence studies of the finite cell method
Persistent Homology in Sparse Regression and its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that
determines the sparsity of the system. How to choose the right tuning parameter
is a fundamental and difficult problem in learning the sparse system. In this
paper, by treating the the tuning parameter as an additional dimension,
persistent homological structures over the parameter space is introduced and
explored. The structures are then further exploited in speeding up the
computation using the proposed soft-thresholding technique. The topological
structures are further used as multivariate features in the tensor-based
morphometry (TBM) in characterizing white matter alterations in children who
have experienced severe early life stress and maltreatment. These analyses
reveal that stress-exposed children exhibit more diffuse anatomical
organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between a phrase and a version of that phrase where words have been
replaced by their synonyms. Different ways of representing such phrases exist
(e.g., vectors [1] or language models [2]), and the choice of representation
affects the measurement of semantic similarity.
We propose a new compositionality detection method that represents phrases as
ranked lists of term weights. Our method approximates the semantic similarity
between two ranked list representations using a range of well-known distance
and correlation metrics. In contrast to most state-of-the-art approaches in
compositionality detection, our method is completely unsupervised. Experiments
with a publicly available dataset of 1048 human-annotated phrases shows that,
compared to strong supervised baselines, our approach provides superior
measurement of compositionality using any of the distance and correlation
metrics considered
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