2,161 research outputs found
Heterotic String Corrections from the Dual Type II String
We introduce a method of using the a dual type IIA string to compute
alpha'-corrections to the moduli space of heterotic string compactifications.
In particular we study the hypermultiplet moduli space of a heterotic string on
a K3 surface. One application of this machinery shows that type IIB strings
compactified on a Calabi-Yau space suffer from worldsheet instantons, spacetime
instantons and, in addition, "mixed" instantons which in a sense are both
worldsheet and spacetime. As another application we look at the hyperkaehler
limit of the moduli space in which the K3 surface becomes an ALE space. This is
a variant of the "geometric engineering" method used for vector multiplet
moduli space and should be applicable to a wide range of examples. In
particular we reproduce Sen and Witten's result for the heterotic string on an
A1 singularity and a trivial bundle and generalize this to a collection of E8
point-like instantons on an ALE space.Comment: 21 pages, 5 figures, refs adde
PURCHASING AND INVENTORY MANAGEMENT IN SCENCE-BASED INDUSTRIES
Inventory Management has been widely discussed in the
literature. Recently, the so called "Just in Timeâ method
received extensive publicity and was claimed to be one of the
major factors of the Japanese industrial success. This, in turn,
promoted a large campaign in the rest of the industrialized
world, to adopt and imitate the "Just in Timeâ (JIT) policy.
Corporate and plant managers focused attention and set up goals
as to reach as closely as possible the Japanese inventory levels.
Quite often, adoption of JIT disregarded the totally different
nature of the business their companies engaged in, relative to
Japanese industry.
This paper clarifies the differences between two different
industrial models: The "Assembly Linesâ model versus the Hi-Tech
Job Shop "Science Basedâ model and prescribes the inventory
strategy appropriate for each of those models. It is shown that
a fully automated Assembly Line type factory requires a âJust in
Timeâ (minimal holding costs) inventory strategy, while the
Science Based type should follow a more elaborate âoptimal
Penaltyâ type of policy.Information Systems Working Papers Serie
Dipolar Bose gases: Many-body versus mean-field description
We characterize zero-temperature dipolar Bose gases under external spherical
confinement as a function of the dipole strength using the essentially exact
many-body diffusion Monte Carlo (DMC) technique. We show that the DMC energies
are reproduced accurately within a mean-field framework if the variation of the
s-wave scattering length with the dipole strength is accounted for properly.
Our calculations suggest stability diagrams and collapse mechanisms of dipolar
Bose gases that differ significantly from those previously proposed in the
literature
D-branes, Discrete Torsion and the McKay Correspondence
We analyze the D-branes of a type IIB string theory on an orbifold
singularity including the possibility of discrete torsion following the work of
Douglas et al. First we prove some general results about the moduli space of a
point associated to the "regular representation" of the orbifold group. This
includes some analysis of the "wrapped branes" which necessarily appear when
the orbifold singularity is not isolated. Next we analyze the stringy homology
of the orbifold using the McKay correspondence and the relationship between
K-theory and homology. We find that discrete torsion and torsion in this
stringy homology are closely-related concepts but that they differ in general.
Lastly we question to what extent the D-1 brane may be thought of as being dual
to a string.Comment: 27 pages, 5 figures, LaTeX2e, minor change
Evaluation of colorectal cancer subtypes and cell lines using deep learning
Colorectal cancer (CRC) is a common cancer with a high mortality rate and rising incidence rate in the developed world. Molecular profiling techniques have been used to study the variability between tumours as well as cancer models such as cell lines, but their translational value is incomplete with current methods. Moreover, first generation computational methods for subtype classification do not make use of multi-omics data in full scale. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We present a novel subtype classification method based on deep learning and apply it to classify CRC tumors using multi-omics data, and further to measure the similarity between tumors and disease models such as cancer cell lines. Multi-omics Autoencoder Integration (maui) efficiently leverages data sets containing copy number alterations, gene expression, and point mutations, and learns clinically important patterns (latent factors) across these data types. Using these latent factors, we propose a refinement of the gold-standard CRC subtypes, and propose best-matching cell lines for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for drug discovery pipelines, biomarker discovery, and target identification
Evaluation of colorectal cancer subtypes and cell lines using deep learning
Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification
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