67 research outputs found
Quantum gravity corrections to the Schwarzschild mass
Vacuum spherically symmetric Einstein gravity in dimensions can be
cast in a two-dimensional conformal nonlinear sigma model form by first
integrating on the -dimensional (hyper)sphere and then performing a
canonical transformation. The conformal sigma model is described by two fields
which are related to the Arnowitt-Deser-Misner mass and to the radius of the
-dimensional (hyper)sphere, respectively. By quantizing perturbatively
the theory we estimate the quantum corrections to the ADM mass of a black hole.Comment: 18 pages, 8 figures, LaTeX2e, uses epsfig package, accepted for
publication in Phys. Rev.
Biharmonic pattern selection
A new model to describe fractal growth is discussed which includes effects
due to long-range coupling between displacements . The model is based on the
biharmonic equation in two-dimensional isotropic defect-free
media as follows from the Kuramoto-Sivashinsky equation for pattern formation
-or, alternatively, from the theory of elasticity. As a difference with
Laplacian and Poisson growth models, in the new model the Laplacian of is
neither zero nor proportional to . Its discretization allows to reproduce a
transition from dense to multibranched growth at a point in which the growth
velocity exhibits a minimum similarly to what occurs within Poisson growth in
planar geometry. Furthermore, in circular geometry the transition point is
estimated for the simplest case from the relation
such that the trajectories become stable at the growing surfaces in a
continuous limit. Hence, within the biharmonic growth model, this transition
depends only on the system size and occurs approximately at a distance far from a central seed particle. The influence of biharmonic patterns on
the growth probability for each lattice site is also analysed.Comment: To appear in Phys. Rev. E. Copies upon request to
[email protected]
Ultracold Atoms in 1D Optical Lattices: Mean Field, Quantum Field, Computation, and Soliton Formation
In this work, we highlight the correspondence between two descriptions of a
system of ultracold bosons in a one-dimensional optical lattice potential: (1)
the discrete nonlinear Schr\"{o}dinger equation, a discrete mean-field theory,
and (2) the Bose-Hubbard Hamiltonian, a discrete quantum-field theory. The
former is recovered from the latter in the limit of a product of local coherent
states. Using a truncated form of these mean-field states as initial
conditions, we build quantum analogs to the dark soliton solutions of the
discrete nonlinear Schr\"{o}dinger equation and investigate their dynamical
properties in the Bose-Hubbard Hamiltonian. We also discuss specifics of the
numerical methods employed for both our mean-field and quantum calculations,
where in the latter case we use the time-evolving block decimation algorithm
due to Vidal.Comment: 14 pages, 2 figures; to appear in Journal of Mathematics and
Computers in Simulatio
Collisionless Relaxation in Galactic Dynamics and the Evolution of Long Range Order
This talk provides a critical assessment of collisionless galactic dynamics,
focusing on the interpretation and limitations of the collisionless Boltzmann
equation and the physical mechanisms associated with collisionless relaxation.
Numerical and theoretical arguments are presented to motivate the idea that the
evolution of a system far from equilibrium should be interpreted as involving
nonlinear gravitational Landau damping, which implies a greater overall
coherence and remembrance of initial conditions than is implicit in the
conventional theory of violent relaxation.Comment: 20 pages, plain latex, no macros required, no figures a talk
presented at the 1997 Florida Workshop on Nonlinear Astronomy and Physics, to
appear in Annals of the New York Academy of Science
Microneedle array delivered recombinant coronavirus vaccines: Immunogenicity and rapid translational development
Background: Coronaviruses pose a serious threat to global health as evidenced by Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and COVID-19. SARS Coronavirus (SARS-CoV), MERS Coronavirus (MERS-CoV), and the novel coronavirus, previously dubbed 2019-nCoV, and now officially named SARS-CoV-2, are the causative agents of the SARS, MERS, and COVID-19 disease outbreaks, respectively. Safe vaccines that rapidly induce potent and long-lasting virus-specific immune responses against these infectious agents are urgently needed
LAr1-ND: Testing Neutrino Anomalies with Multiple LArTPC Detectors at Fermilab
This white paper describes LAr1-ND and the compelling physics it brings first
in Phase 1 and next towards the full LAr1 program. In addition, LAr1-ND serves
as a key step in the development toward large-scale LArTPC detectors. Its
development goals will encompass testing existing and possibly innovative
designs for LBNE while at the same time providing a training ground for teams
working towards LBNE combining timely neutrino physics with experience in
detector development
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.
In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA
Combining meta-learning and search techniques to select parameters for support vector machines
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