Thermal correlators of anyons in two dimensions

The anyon fields have trivial $\alpha$-commutator for $\alpha$ not integer. For integer $\alpha$ the commutators become temperature-dependent operator valued distributions. The $n$-point functions do not factorize as for quasifree states.Comment: 14 pages, LaTeX (misprints corrected, a reference added

Evolution Kernels of Twist-3 Light-Ray Operators in Polarized Deep Inelastic Scattering

The twist three contributions to the $Q^2$-evolution of the spin-dependent structure function $g_2(x)$ are considered in the non-local operator product approach. Starting from the perturbative expansion of the T-product of two electromagnetic currents, we introduce the nonlocal light-cone expansion proved by Anikin and Zavialov and determine the physical relevant set of light-ray operators of twist three. Using the equations of motion we show the equivalence of these operators to the Shuryak-Vainshtein operators plus the mass operator, and we determine their evolution kernels using the light-cone gauge with the Leibbrandt-Mandelstam prescription. The result of Balitsky and Braun for the twist three evolution kernel (nonsinglet case) is confirmed.Comment: 7 pages, LaTeX, Talk given at the workshop "QCD and QED in Higher Order", Rheinsberg, April 21-26, 199

SiPM and front-end electronics development for Cherenkov light detection

The Italian Institute of Nuclear Physics (INFN) is involved in the development of a demonstrator for a SiPM-based camera for the Cherenkov Telescope Array (CTA) experiment, with a pixel size of 6$\times$6 mm$^2$. The camera houses about two thousands electronics channels and is both light and compact. In this framework, a R&D program for the development of SiPMs suitable for Cherenkov light detection (so called NUV SiPMs) is ongoing. Different photosensors have been produced at Fondazione Bruno Kessler (FBK), with different micro-cell dimensions and fill factors, in different geometrical arrangements. At the same time, INFN is developing front-end electronics based on the waveform sampling technique optimized for the new NUV SiPM. Measurements on 1$\times$1 mm$^2$, 3$\times$3 mm$^2$, and 6$\times$6 mm$^2$ NUV SiPMs coupled to the front-end electronics are presentedComment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

Social facilitation on the development of foraging behaviors in a population of autonomous robots

Abstract. In this paper we propose an adaptive algorithm based on a combination of selective reproduction, individual learning, and social learning. Social learning consists of a simple facilitation process that regulates the strength of individual learning on the basis of the number of individuals located nearby. By testing this model in an experimental scenario, in which a population of 10 mobile robots has to develop a simple foraging behavior, we demonstrate how the model proposed produces effective results. By comparing the results obtained in different experimental conditions we also show how the method proposed outperforms other alternative algorithms based on genetic evolution or individual learning. Finally, we briefly discuss how the model proposed can help us to understand the role of social learning in biological organisms

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

Regularizing Portfolio Optimization

The optimization of large portfolios displays an inherent instability to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification "pressure". This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade-off between the two, depending on the size of the available data set

Anyons and the Bose-Fermi duality in the finite-temperature Thirring model

Solutions to the Thirring model are constructed in the framework of algebraic QFT. It is shown that for all positive temperatures there are fermionic solutions only if the coupling constant is $\lambda=\sqrt{2(2n+1)\pi}, n\in {\bf N}$. These fermions are inequivalent and only for $n=1$ they are canonical fields. In the general case solutions are anyons. Different anyons (which are uncountably many) live in orthogonal spaces and obey dynamical equations (of the type of Heisenberg's "Urgleichung") characterized by the corresponding values of the statistic parameter. Thus statistic parameter turns out to be related to the coupling constant $\lambda$ and the whole Hilbert space becomes non-separable with a different "Urgleichung" satisfied in each of its sectors. This feature certainly cannot be seen by any power expansion in $\lambda$. Moreover, since the latter is tied to the statistic parameter, it is clear that such an expansion is doomed to failure and will never reveal the true structure of the theory. The correlation functions in the temperature state for the canonical dressed fermions are shown by us to coincide with the ones for bare fields, that is in agreement with the uniqueness of the $\tau$-KMS state over the CAR algebra ($\tau$ being the shift automorphism). Also the $\alpha$-anyon two-point function is evaluated and for scalar field it reproduces the result that is known from the literature.Comment: 25 pages, LaTe