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