1,484 research outputs found
Gravitational Collapse of a Shell of Quantized Matter
The semi-classical collapse, including lowest order back-reaction, of a thin
shell of self-gravitating quantized matter is illustrated. The conditions for
which self-gravitating matter forms a thin shell are first discussed and an
effective Lagrangian for such matter is obtained. The matter-gravity system is
then quantized, the semi-classical limit for gravitation is taken and the
method of adiabatic invariants is applied to the resulting time dependent
matter Hamiltonian. The governing equations are integrated numerically, for
suitable initial conditions, in order to illustrate the effect of
back-reaction, due to the creation of matter, in slowing down the collapse near
the horizon.Comment: 20 pages, 1 eps figure. Problem with figure fixe
ï»żThe Rainbow Prim Algorithm for Selecting Putative Orthologous Protein Sequences
We present a selection method designed for eliminating species redundancy in clusters of putative orthologous sequences, to be applied as a post-processing procedure to pre-clustered data obtained from other methods. The algorithm can always zero-out the cluster redundancy while preserving the number of species of the original cluster
Theoretical survey of tidal-charged black holes at the LHC
We analyse a family of brane-world black holes which solve the effective
four-dimensional Einstein equations for a wide range of parameters related to
the unknown bulk/brane physics. We first constrain the parameters using known
experimental bounds and, for the allowed cases, perform a numerical analysis of
their time evolution, which includes accretion through the Earth. The study is
aimed at predicting the typical behavior one can expect if such black holes
were produced at the LHC. Most notably, we find that, under no circumstances,
would the black holes reach the (hazardous) regime of Bondi accretion.
Nonetheless, the possibility remains that black holes live long enough to
escape from the accelerator (and even from the Earth's gravitational field) and
result in missing energy from the detectors.Comment: RevTeX4, 12 pages, 4 figures, 5 tables, minor changes to match the
accepted version in JHE
Gravitational Collapse of a Radiating Shell
We study the collapse of a self-gravitating and radiating shell. Matter
constituting the shell is quantized and the construction is viewed as a
semiclassical model of possible black hole formation. It is shown that the
shell internal degrees of freedom are excited by the quantum non-adiabaticity
of the collapse and, consequently, on coupling them to a massless scalar field,
the collapsing matter emits a burst of coherent (thermal) radiation.Comment: LaTeX, 34 pages, 21 EPS figures include
Brane-world black holes and the scale of gravity
A particle in four dimensions should behave like a classical black hole if
the horizon radius is larger than the Compton wavelength or, equivalently, if
its degeneracy (measured by entropy in units of the Planck scale) is large. For
spherically symmetric black holes in 4 + d dimensions, both arguments again
lead to a mass threshold MC and degeneracy scale Mdeg of the order of the
fundamental scale of gravity MG. In the brane-world, deviations from the
Schwarzschild metric induced by bulk effects alter the horizon radius and
effective four-dimensional Euclidean action in such a way that MC \simeq Mdeg
might be either larger or smaller than MG. This opens up the possibility that
black holes exist with a mass smaller than MG and might be produced at the LHC
even if M>10 TeV, whereas effects due to bulk graviton exchanges remain
undetectable because suppressed by inverse powers of MG. Conversely, even if
black holes are not found at the LHC, it is still possible that MC>MG and MG
\simeq 1TeV.Comment: 4 pages, no figur
Adiabatic Invariant Treatment of a Collapsing Sphere of Quantized Dust
The semiclassical collapse of a sphere of quantized dust is studied. A
Born-Oppenheimer decomposition is performed for the wave function of the system
and the semiclassical limit is considered for the gravitational part. The
method of adiabatic invariants for time dependent Hamiltonians is then employed
to find (approximate) solutions to the quantum dust equations of motions. This
allows us to obtain corrections to the adiabatic approximation of the dust
states associated with the time evolution of the metric. The diverse
non-adiabatic corrections are generally associated with particle (dust)
creation and related fluctuations. The back-reaction due to the dominant
contribution to particle creation is estimated and seen to slow-down the
collapse.Comment: LaTeX, 16 pages, no figures, final version to appear in Class. and
Quantum Gravit
Minimum length effects in black hole physics
We review the main consequences of the possible existence of a minimum
measurable length, of the order of the Planck scale, on quantum effects
occurring in black hole physics. In particular, we focus on the ensuing minimum
mass for black holes and how modified dispersion relations affect the Hawking
decay, both in four space-time dimensions and in models with extra spatial
dimensions. In the latter case, we briefly discuss possible phenomenological
signatures.Comment: 29 pages, 12 figures. To be published in "Quantum Aspects of Black
Holes", ed. X. Calmet (Springer, 2014
Quantum Black Holes from Cosmic Rays
We investigate the possibility for cosmic ray experiments to discover
non-thermal small black holes with masses in the TeV range. Such black holes
would result due to the impact between ultra high energy cosmic rays or
neutrinos with nuclei from the upper atmosphere and decay instantaneously. They
could be produced copiously if the Planck scale is in the few TeV region. As
their masses are close to the Planck scale, these holes would typically decay
into two particles emitted back-to-back. Depending on the angles between the
emitted particles with respect to the center of mass direction of motion, it is
possible for the simultaneous showers to be measured by the detectors.Comment: 6 pages, 3 figure
Machine learning solutions for predicting proteinâprotein interactions
Proteins are social molecules. Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein-aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein-binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never-seen-before examples. ML tools address the problem of proteinâprotein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML-computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind datasets. Results indicate that the state-of-the-art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI-functional interaction
Finding functional motifs in protein sequences with deep learning and natural language models
Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machine learning and encoding schemas are available for predicting different structural/functional motifs. Particularly interesting is the adoption of protein language models to encode proteins in addition to evolution information and physicochemical parameters. A thorough analysis of recent predictors developed for annotating transmembrane regions, sorting signals, lipidation and phosphorylation sites allows to investigate the state-of-the-art focusing on the relevance of protein language models for the different tasks. This highlights that more experimental data are necessary to exploit available powerful machine learning methods
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