3,613 research outputs found
Scalar Material Reference Systems and Loop Quantum Gravity
In the past, the possibility to employ (scalar) material reference systems in
order to describe classical and quantum gravity directly in terms of gauge
invariant (Dirac) observables has been emphasised frequently. This idea has
been picked up more recently in Loop Quantum Gravity (LQG) with the aim to
perform a reduced phase space quantisation of the theory thus possibly avoiding
problems with the (Dirac) operator constraint quantisation method for
constrained system. In this work, we review the models that have been studied
on the classical and/or the quantum level and parametrise the space of theories
so far considered. We then describe the quantum theory of a model that, to the
best of our knowledge, so far has only been considered classically. This model
could arguably called the optimal one in this class of models considered as it
displays the simplest possible true Hamiltonian while at the same time reducing
all constraints of General Relativity.Comment: 28 pages, some references were correcte
Born--Oppenheimer decomposition for quantum fields on quantum spacetimes
Quantum Field Theory on Curved Spacetime (QFT on CS) is a well established
theoretical framework which intuitively should be a an extremely effective
description of the quantum nature of matter when propagating on a given
background spacetime. If one wants to take care of backreaction effects, then a
theory of quantum gravity is needed. It is now widely believed that such a
theory should be formulated in a non-perturbative and therefore background
independent fashion. Hence, it is a priori a puzzle how a background dependent
QFT on CS should emerge as a semiclassical limit out of a background
independent quantum gravity theory. In this article we point out that the
Born-Oppenheimer decomposition (BOD) of the Hilbert space is ideally suited in
order to establish such a link, provided that the Hilbert space representation
of the gravitational field algebra satisfies an important condition. If the
condition is satisfied, then the framework of QFT on CS can be, in a certain
sense, embedded into a theory of quantum gravity. The unique representation of
the holonomy-flux algebra underlying Loop Quantum Gravity (LQG) violates that
condition. While it is conceivable that the condition on the representation can
be relaxed, for convenience in this article we consider a new classical
gravitational field algebra and a Hilbert space representation of its
restriction to an algebraic graph for which the condition is satisfied. An
important question that remains and for which we have only partial answers is
how to construct eigenstates of the full gravity-matter Hamiltonian whose BOD
is confined to a small neighbourhood of a physically interesting vacuum
spacetime.Comment: 38 pages, 2 figure
THERAPEUTIC DEVELOPMENT FOR CYSTIC FIBROSIS
This thesis considers the aspects of therapeutics development for Cystic Fibrosis (CF). The studies are directed at the development of a new therapeutic outcome measurement for evaluating the performance of CF medications. This imaging-based outcome measures the absorption of a small-molecular radiopharmaceutical, Diethylene triamine pentaacetic acid (DTPA) from the airways as a surrogate measure of liquid absorption. Airway liquid hyper-absorption is a key aspect of CF lung disease that would be expected to correct rapidly after administration of a successful therapy. In vivo pilot studies of this technique have been previously performed at our center [1]. Here we report the results of in vitro studies performed to better define the mechanism underpinning our method and define its utility and limitations
Fr\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery
The new wave of successful generative models in machine learning has
increased the interest in deep learning driven de novo drug design. However,
assessing the performance of such generative models is notoriously difficult.
Metrics that are typically used to assess the performance of such generative
models are the percentage of chemically valid molecules or the similarity to
real molecules in terms of particular descriptors, such as the partition
coefficient (logP) or druglikeness. However, method comparison is difficult
because of the inconsistent use of evaluation metrics, the necessity for
multiple metrics, and the fact that some of these measures can easily be
tricked by simple rule-based systems. We propose a novel distance measure
between two sets of molecules, called Fr\'echet ChemNet distance (FCD), that
can be used as an evaluation metric for generative models. The FCD is similar
to a recently established performance metric for comparing image generation
methods, the Fr\'echet Inception Distance (FID). Whereas the FID uses one of
the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
deep neural network called ChemNet, which was trained to predict drug
activities. Thus, the FCD metric takes into account chemically and biologically
relevant information about molecules, and also measures the diversity of the
set via the distribution of generated molecules. The FCD's advantage over
previous metrics is that it can detect if generated molecules are a) diverse
and have similar b) chemical and c) biological properties as real molecules. We
further provide an easy-to-use implementation that only requires the SMILES
representation of the generated molecules as input to calculate the FCD.
Implementations are available at: https://www.github.com/bioinf-jku/FCDComment: Implementations are available at:
https://www.github.com/bioinf-jku/FC
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