39,006 research outputs found
On Lorentz-Violating Supersymmetric Quantum Field Theories
We study the possibility of constructing Lorentz-violating supersymmetric
quantum field theories under the assumption that these theories have to be
described by lagrangians which are renormalizable by weighted power counting.
Our investigation starts from the observation that at high energies
Lorentz-violation and the usual supersymmetry algebra are algebraically
compatible. Demanding linearity of the supercharges we see that the requirement
of renormalizability drastically restricts the set of possible
Lorentz-violating supersymmetric theories. In particular, in the case of
supersymmetric gauge theories the weighted power counting has to coincide with
the usual one and the only Lorentz-violating operators are introduced by some
weighted constant c that explicitly appears in the supersymmetry algebra. This
parameter does not renormalize and has to be very close to the speed of light
at low energies in order to satisfy the strict experimental bounds on Lorentz
violation. The only possible models with non trivial Lorentz-violating
operators involve neutral chiral superfields and do not have a gauge invariant
extension. We conclude that, under the assumption that high-energy physics can
be described by a renormalizable Lorentz-violating extensions of the Standard
Model, the Lorentz fine tuning problem does not seem solvable by the
requirement of supersymmetry.Comment: 22 pages, 2 figure
Complex Networks and Symmetry I: A Review
In this review we establish various connections between complex networks and
symmetry. While special types of symmetries (e.g., automorphisms) are studied
in detail within discrete mathematics for particular classes of deterministic
graphs, the analysis of more general symmetries in real complex networks is far
less developed. We argue that real networks, as any entity characterized by
imperfections or errors, necessarily require a stochastic notion of invariance.
We therefore propose a definition of stochastic symmetry based on graph
ensembles and use it to review the main results of network theory from an
unusual perspective. The results discussed here and in a companion paper show
that stochastic symmetry highlights the most informative topological properties
of real networks, even in noisy situations unaccessible to exact techniques.Comment: Final accepted versio
Weighted Proportional Losses Solution
We propose and characterize a new solution for problems with asymmetric bargaining power among the agents that we named weighted proportional losses solution. It is specially interesting when agents are bargaining under restricted probabilistic uncertainty. The weighted proportional losses assigns to each agent losses proportional to her ideal utility and also proportional to her bargaining power. This solution is always individually rational, even for 3 or more agents and it can be seen as the normalized weighted equal losses solution. When bargaining power among the agents is equal, the weighted proportional losses solution becomes the Kalai-Smorodinsky solution. We characterize our solution in the basis of restricted monotonicity and restricted concavity. A consequence of this result is an alternative characterization of Kalai-Smorodinsky solution which includes contexts with some kind of uncertainty. Finally we show that weighted proportional losses solution satisfyies desirable properties as are strong Pareto optimality for 2 agents and continuity also fulfilled by Kalai-Smorodinsky solution, that are not satisfied either by weighted or asymmetric Kalai-Smorodinsky solutions.
Weighted power counting and chiral dimensional regularization
We define a modified dimensional-regularization technique that overcomes
several difficulties of the ordinary technique, and is specially designed to
work efficiently in chiral and parity violating quantum field theories, in
arbitrary dimensions greater than 2. When the dimension of spacetime is
continued to complex values, spinors, vectors and tensors keep the components
they have in the physical dimension, therefore the matrices are the
standard ones. Propagators are regularized with the help of evanescent
higher-derivative kinetic terms, which are of the Majorana type in the case of
chiral fermions. If the new terms are organized in a clever way, weighted power
counting provides an efficient control on the renormalization of the theory,
and allows us to show that the resulting chiral dimensional regularization is
consistent to all orders. The new technique considerably simplifies the proofs
of properties that hold to all orders, and makes them suitable to be
generalized to wider classes of models. Typical examples are the
renormalizability of chiral gauge theories and the Adler-Bardeen theorem. The
difficulty of explicit computations, on the other hand, may increase.Comment: 41 pages; v2: minor changes, PRD versio
Birational cobordism invariance of uniruled symplectic manifolds
A symplectic manifold is called {\em (symplectically) uniruled}
if there is a nonzero genus zero GW invariant involving a point constraint. We
prove that symplectic uniruledness is invariant under symplectic blow-up and
blow-down. This theorem follows from a general Relative/Absolute correspondence
for a symplectic manifold together with a symplectic submanifold. A direct
consequence is that symplectic uniruledness is a symplectic birational
invariant. Here we use Guillemin and Sternberg's notion of cobordism as the
symplectic analogue of the birational equivalence.Comment: To appear in Invent. Mat
IVUS-based histology of atherosclerotic plaques: improving longitudinal resolution
Although Virtual Histology (VH) is the in-vivo gold standard for atherosclerosis plaque characterization in IVUS images, it suffers from a poor longitudinal resolution due to ECG-gating. In this paper, we propose an image- based approach to overcome this limitation. Since each tissue have different echogenic characteristics, they show in IVUS images different local frequency components. By using Redundant Wavelet Packet Transform (RWPT), IVUS images are decomposed in multiple sub-band images. To encode the textural statistics of each resulting image, run-length features are extracted from the neighborhood centered on each pixel. To provide the best discrimination power according to these features, relevant sub-bands are selected by using Local Discriminant Bases (LDB) algorithm in combination with Fisher’s criterion. A structure of weighted multi-class SVM permits the classification of the extracted feature vectors into three tissue classes, namely fibro-fatty, necrotic core and dense calcified tissues. Results shows the superiority of our approach with an overall accuracy of 72% in comparison to methods based on Local Binary Pattern and Co-occurrence, which respectively give accuracy rates of 70% and 71%
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