13,456 research outputs found
Point configurations that are asymmetric yet balanced
A configuration of particles confined to a sphere is balanced if it is in
equilibrium under all force laws (that act between pairs of points with
strength given by a fixed function of distance). It is straightforward to show
that every sufficiently symmetrical configuration is balanced, but the converse
is far from obvious. In 1957 Leech completely classified the balanced
configurations in R^3, and his classification is equivalent to the converse for
R^3. In this paper we disprove the converse in high dimensions. We construct
several counterexamples, including one with trivial symmetry group.Comment: 10 page
Point configurations that are asymmetric yet balanced
A configuration of particles confined to a sphere is balanced if it is in
equilibrium under all force laws (that act between pairs of points with
strength given by a fixed function of distance). It is straightforward to show
that every sufficiently symmetrical configuration is balanced, but the converse
is far from obvious. In 1957 Leech completely classified the balanced
configurations in R^3, and his classification is equivalent to the converse for
R^3. In this paper we disprove the converse in high dimensions. We construct
several counterexamples, including one with trivial symmetry group.Comment: 10 page
An asymptotic existence result on compressed sensing matrices
For any rational number and all sufficiently large we give a
deterministic construction for an compressed
sensing matrix with -recoverability where . Our
method uses pairwise balanced designs and complex Hadamard matrices in the
construction of -equiangular frames, which we introduce as a
generalisation of equiangular tight frames. The method is general and produces
good compressed sensing matrices from any appropriately chosen pairwise
balanced design. The -recoverability performance is specified as a
simple function of the parameters of the design. To obtain our asymptotic
existence result we prove new results on the existence of pairwise balanced
designs in which the numbers of blocks of each size are specified.Comment: 15 pages, no figures. Minor improvements and updates in February 201
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
The use of orthogonal projections on high-dimensional input and target data
in learning frameworks is studied. First, we investigate the relations between
two standard objectives in dimension reduction, preservation of variance and of
pairwise relative distances. Investigations of their asymptotic correlation as
well as numerical experiments show that a projection does usually not satisfy
both objectives at once. In a standard classification problem we determine
projections on the input data that balance the objectives and compare
subsequent results. Next, we extend our application of orthogonal projections
to deep learning tasks and introduce a general framework of augmented target
loss functions. These loss functions integrate additional information via
transformations and projections of the target data. In two supervised learning
problems, clinical image segmentation and music information classification, the
application of our proposed augmented target loss functions increase the
accuracy
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