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

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$_3$, 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.

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

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$_3$ and Thirring model as a gauge theory, respectively) due to the radiative corrections.Comment: 14 pages, LaTex, no figure

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

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

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/

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

Closed Loop Pulsating Heat Pipes: Ground and Microgravity experiments

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