9,954 research outputs found
Memory-Adjustable Navigation Piles with Applications to Sorting and Convex Hulls
We consider space-bounded computations on a random-access machine (RAM) where
the input is given on a read-only random-access medium, the output is to be
produced to a write-only sequential-access medium, and the available workspace
allows random reads and writes but is of limited capacity. The length of the
input is elements, the length of the output is limited by the computation,
and the capacity of the workspace is bits for some predetermined
parameter . We present a state-of-the-art priority queue---called an
adjustable navigation pile---for this restricted RAM model. Under some
reasonable assumptions, our priority queue supports and
in worst-case time and in worst-case time for any . We show how to use this
data structure to sort elements and to compute the convex hull of
points in the two-dimensional Euclidean space in
worst-case time for any . Following a known lower bound for the
space-time product of any branching program for finding unique elements, both
our sorting and convex-hull algorithms are optimal. The adjustable navigation
pile has turned out to be useful when designing other space-efficient
algorithms, and we expect that it will find its way to yet other applications.Comment: 21 page
Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume
A local principal curve algorithm has been implemented in three dimensions
for automated track and shower reconstruction of neutrino interactions in a
liquid argon time projection chamber. We present details of the algorithm and
characterise its performance on simulated data sets.Comment: 14 pages, 17 figures; typing correction to Eq 5, the definition of
the local covariance matri
Critically fast pick-and-place with suction cups
Fast robotics pick-and-place with suction cups is a crucial component in the
current development of automation in logistics (factory lines, e-commerce,
etc.). By "critically fast" we mean the fastest possible movement for
transporting an object such that it does not slip or fall from the suction cup.
The main difficulties are: (i) handling the contact between the suction cup and
the object, which fundamentally involves kinodynamic constraints; and (ii)
doing so at a low computational cost, typically a few hundreds of milliseconds.
To address these difficulties, we propose (a) a model for suction cup contacts,
(b) a procedure to identify the contact stability constraint based on that
model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary
geometric paths under the identified contact stability constraint. We
experimentally validate the proposed pipeline on a physical robot system: the
cycle time for a typical pick-and-place task was less than 5 seconds, planning
and execution times included. The full pipeline is released as open-source for
the robotics community.Comment: 7 pages, 5 figure
Phase behavior and morphology of multicomponent liquid mixtures
Multicomponent systems are ubiquitous in nature and industry. While the
physics of few-component liquid mixtures (i.e., binary and ternary ones) is
well-understood and routinely taught in undergraduate courses, the
thermodynamic and kinetic properties of -component mixtures with have
remained relatively unexplored. An example of such a mixture is provided by the
intracellular fluid, in which protein-rich droplets phase separate into
distinct membraneless organelles. In this work, we investigate equilibrium
phase behavior and morphology of -component liquid mixtures within the
Flory-Huggins theory of regular solutions. In order to determine the number of
coexisting phases and their compositions, we developed a new algorithm for
constructing complete phase diagrams, based on numerical convexification of the
discretized free energy landscape. Together with a Cahn-Hilliard approach for
kinetics, we employ this method to study mixtures with and
components. We report on both the coarsening behavior of such systems, as well
as the resulting morphologies in three spatial dimensions. We discuss how the
number of coexisting phases and their compositions can be extracted with
Principal Component Analysis (PCA) and K-Means clustering algorithms. Finally,
we discuss how one can reverse engineer the interaction parameters and volume
fractions of components in order to achieve a range of desired packing
structures, such as nested `Russian dolls' and encapsulated Janus droplets.Comment: 16 pages, 11 figures + hyperlinks to 7 video
3D/2D Registration of Mapping Catheter Images for Arrhythmia Interventional Assistance
Radiofrequency (RF) catheter ablation has transformed treatment for
tachyarrhythmias and has become first-line therapy for some tachycardias. The
precise localization of the arrhythmogenic site and the positioning of the RF
catheter over that site are problematic: they can impair the efficiency of the
procedure and are time consuming (several hours). Electroanatomic mapping
technologies are available that enable the display of the cardiac chambers and
the relative position of ablation lesions. However, these are expensive and use
custom-made catheters. The proposed methodology makes use of standard catheters
and inexpensive technology in order to create a 3D volume of the heart chamber
affected by the arrhythmia. Further, we propose a novel method that uses a
priori 3D information of the mapping catheter in order to estimate the 3D
locations of multiple electrodes across single view C-arm images. The monoplane
algorithm is tested for feasibility on computer simulations and initial canine
data.Comment: International Journal of Computer Science Issues, IJCSI, Volume 4,
Issue 2, pp10-19, September 200
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Nonnegative matrix factorization (NMF) has been shown recently to be
tractable under the separability assumption, under which all the columns of the
input data matrix belong to the convex cone generated by only a few of these
columns. Bittorf, Recht, R\'e and Tropp (`Factoring nonnegative matrices with
linear programs', NIPS 2012) proposed a linear programming (LP) model, referred
to as Hottopixx, which is robust under any small perturbation of the input
matrix. However, Hottopixx has two important drawbacks: (i) the input matrix
has to be normalized, and (ii) the factorization rank has to be known in
advance. In this paper, we generalize Hottopixx in order to resolve these two
drawbacks, that is, we propose a new LP model which does not require
normalization and detects the factorization rank automatically. Moreover, the
new LP model is more flexible, significantly more tolerant to noise, and can
easily be adapted to handle outliers and other noise models. Finally, we show
on several synthetic datasets that it outperforms Hottopixx while competing
favorably with two state-of-the-art methods.Comment: 27 page; 4 figures. New Example, new experiment on the Swimmer data
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