4,429 research outputs found
Torus partition functions and spectra of gauged linear sigma models
Worldsheet (0,2) gauged linear sigma models are often used to study
supersymmetric heterotic string compactifications with non-trivial vector
bundles. We make use of supersymmetric localization techniques to determine
their one-loop partition functions. In particular we derive conditions which
ensure that the full partition function is modular invariant and we propose a
method to determine the massless and massive target space matter spectrum.Comment: 4 pages LaTeX; v2: important technical issue resolved, most results
essentially unchange
Line bundle embeddings for heterotic theories
In heterotic string theories consistency requires the introduction of a
non-trivial vector bundle. This bundle breaks the original ten-dimensional
gauge groups or for the
supersymmetric heterotic string theories and for the non-supersymmetric tachyon-free theory to smaller
subgroups. A vast number of MSSM-like models have been constructed up to now,
most of which describe the vector bundle as a sum of line bundles. However,
there are several different ways of describing these line bundles and their
embedding in the ten-dimensional gauge group. We recall and extend these
different descriptions and explain how they can be translated into each other.Comment: 29+1 pages, 7 tables, 1 figure, typos corrected, clarifying remarks
and references adde
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 0.53 {\mu}m
and 4.09 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie
Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid
Flexible energy resources can help balance the power grid by providing
different types of ancillary services. However, the balancing potential of most
types of resources is restricted by physical constraints such as the size of
their energy buffer, limits on power-ramp rates, or control delays. Using the
example of Secondary Frequency Regulation, this paper shows how the flexibility
of various resources can be exploited more efficiently by considering multiple
resources with complementary physical properties and controlling them in a
coordinated way. To this end, optimal adjustable control policies are computed
based on robust optimization. Our problem formulation takes into account power
ramp-rate constraints explicitly, and accurately models the different
timescales and lead times of the energy and reserve markets. Simulations
demonstrate that aggregations of select resources can offer significantly more
regulation capacity than the resources could provide individually.Comment: arXiv admin note: text overlap with arXiv:1804.0389
Fast conditional density estimation for quantitative structure-activity relationships
Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional methods for conditional density estimation, they are based on generic machine learning schemes, more specifically, class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training. Therefore, generic machine-learning based methods for conditional density estimation may be a good and fast option for quantifying uncertainty in QSAR modeling.http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/181
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