9,682 research outputs found
Causal Theory for the Gauged Thirring Model
We consider the (2+1)-dimensional massive Thirring model as a gauge theory,
with one fermion flavor, in the framework of the causal perturbation theory and
address the problem of dynamical mass generation for the gauge boson. In this
context we get an unambiguous expression for the coefficient of the induced
Chern-Simons term.Comment: LaTex, 21 pages, no figure
Axial Anomaly through Analytic Regularization
In this work we consider the 2-point Green's functions in (1+1) dimensional
quantum electrodynamics and show that the correct implementation of analytic
regularization gives a gauge invariant result for the vaccum polarization
amplitude and the correct coefficient for the axial anomaly.Comment: 8 pages, LaTeX, no figure
Radiative Corrections for the Gauged Thirring Model in Causal Perturbation Theory
We evaluate the one-loop fermion self-energy for the gauged Thirring model in
(2+1) dimensions, with one massive fermion flavor, in the framework of the
causal perturbation theory. In contrast to QED, the corresponding two-point
function turns out to be infrared finite on the mass shell. Then, by means of a
Ward identity, we derive the on-shell vertex correction and discuss the role
played by causality for nonrenormalizable theories.Comment: LaTex, 09 pages, no figures. Title changed and introduction enlarged.
To be published in Eur. Phys. J.
Gauged Thirring Model in the Heisenberg Picture
We consider the (2+1)-dimensional gauged Thirring model in the Heisenberg
picture. In this context we evaluate the vacuum polarization tensor as well as
the corrected gauge boson propagator and address the issues of generation of
mass and dynamics for the gauge boson (in the limits of QED and Thirring
model as a gauge theory, respectively) due to the radiative corrections.Comment: 14 pages, LaTex, no figure
Brightness as an Augmentation Technique for Image Classification
Augmentation techniques are crucial for accurately training convolution neural networks (CNNs). Therefore, these techniques have become the preprocessing methods. However, not every augmentation technique can be beneficial, especially those that change the image’s underlying structure, such as color augmentation techniques. In this study, the effect of eight brightness scales was investigated in the task of classifying a large histopathology dataset. Four state-of-the-art CNNs were used to assess each scale’s performance. The use of brightness was not beneficial in all the experiments. Among the different brightness scales, the [0.75–1.00] scale, which closely resembles the original brightness of the images, resulted in the best performance. The use of geometric augmentation yielded better performance than any brightness scale. Moreover, the results indicate that training the CNN without applying any augmentation techniques led to better results than considering brightness augmentation. Therefore, experimental results support the hypothesis that brightness augmentation techniques are not beneficial for image classification using deep-learning models and do not yield any performance gain. Furthermore, brightness augmentation techniques can significantly degrade the model’s performance when they are applied with extreme values
Follow-up of 53 Alzheimer patients with the MODA (Milan Overall Dementia Assessment)
Fifty-three patients affected by Alzheimer's disease entered a longitudinal survey aimed at studying which factors influence the rate of progression, assessed by means of the Milan Overall Dementia Assessment (MODA). The second examination was carried out, on average, after 16 months from the first assessment. Only age proved to influence the decline rate, which was faster in elders
Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions.
Geometric Semantic Genetic Programming (GSGP) is a recently
introduced form of Genetic Programming (GP), rooted
in a geometric theory of representations, that searches directly
the semantic space of functions/programs, rather than
the space of their syntactic representations (e.g., trees) as in
traditional GP. Remarkably, the fitness landscape seen by
GSGP is always – for any domain and for any problem –
unimodal with a linear slope by construction. This has two
important consequences: (i) it makes the search for the optimum
much easier than for traditional GP; (ii) it opens the
way to analyse theoretically in a easy manner the optimisation
time of GSGP in a general setting. The runtime analysis
of GP has been very hard to tackle, and only simplified forms
of GP on specific, unrealistic problems have been studied so
far. We present a runtime analysis of GSGP with various
types of mutations on the class of all Boolean functionsThe authors are grateful to Dirk Sudholt for helping check the proofs. Alberto Moraglio was supported by EPSRC grant EP/I010297/
GANs for Integration of Deterministic Model and Observations in Marine Ecosystem
Monitoring the marine ecosystem can be done via observations (either in-situ or satellite) and via deterministic models. However, each of these methods has some drawbacks: observations can be accurate but insufficient in terms of temporal and spatial coverage, while deterministic models cover the whole marine ecosystem but can be inaccurate. This work aims at developing a deep learning model to reproduce the biogeochemical variables in the Mediterranean Sea, integrating observations and the output of an existing deterministic model of the marine ecosystem. In particular, two deep learning architectures will be proposed and tested: first EmuMed, an emulator of the deterministic model, and then InpMed, which consists of an improvement of the latter by the addition of information provided by in-situ and satellite observations. Results show that EmuMed can successfully reproduce the output of the deterministic model, while ImpMed can successfully make use of the additional information provided, thus improving our ability to monitor the biogeochemical variables in the Mediterranean Sea
Commentary on “Jaws 30”, by W. B. Langdon
While genetic programming has had a huge impact on the research community, it is fair to say that its impact on industry and practitioners has been much smaller. In this commentary we elaborate on this claim and suggest some broad research goals aimed at greatly increasing such impact
Heuristic search of (semi-)bent functions based on cellular automata
An interesting thread in the research of Boolean functions for cryptography and coding theory is the study of secondary constructions: given a known function with a good cryptographic profile, the aim is to extend it to a (usually larger) function possessing analogous properties. In this work, we continue the investigation of a secondary construction based on cellular automata (CA), focusing on the classes of bent and semi-bent functions. We prove that our construction preserves the algebraic degree of the local rule, and we narrow our attention to the subclass of quadratic functions, performing several experiments based on exhaustive combinatorial search and heuristic optimization through Evolutionary Strategies (ES). Finally, we classify the obtained results up to permutation equivalence, remarking that the number of equivalence classes that our CA-XOR construction can successfully extend grows very quickly with respect to the CA diameter
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