Machine design and electron beam control of a single-pass linac for free electron laser : the FERMI@Elettra case study

In classical electromagnetism, a charged particle radiates energy in the form of electromagnetic radiation when it is subject to a force. This effect is the principle behind many useful sources of radiation such as electron synchrotrons and linear accelerators. The main figures of merit of synchrotron radiation sources are (narrow) spectral bandwidth, photon wavelength tunability and brilliance. The periodic motion of particles in a synchrotron makes these machines well-suited for a stable emission at high repetition rate. However, in addition to the synchrotron radiation and complementary to that, a strong need has emerged over the last few years for a source of radiation with extremely high brilliance, close to full coherence, a bandwidth approaching the Fourier limit and a stable and well characterized temporal structure in the femtosecond time domain. Such a source is the single-pass Free Electron Laser (FEL) that, due to Doppler frequency upshifting of emitted radiation by relativistic electrons, is particularly well-suited to generate short wavelength X-ray pulses with peak brilliance many orders of magnitude higher than that generated in modern synchrotrons and with subpicosecond pulse lengths. There are currently no alternative sources that have such high pulse energies and short durations. The investigation domain opened by the new FEL sources covers essentially all basic science fields giving access to explorations of matter in practically unexplored regimes. The scientific opportunities will in fact impact studies of a large number of disciplines encompassing material and biomaterial science, nanoscience, plasma physics, molecular and cluster femto- and nano- physics and chemistry, as well as having various connections to life, environmental, astrophysical and earth science. The FEL high brilliance, high intensity and shot-to-shot stability strongly depends on the electron beam source. Delivering a high quality electron beam and machine flexibility to serve a broad range of potential applications imposes severe requirements on the final electron beam parameters and the machine design. To meet these requirements, the need of a linac design based on extensive studies of possible perturbations that may affect the electron beam dynamics, of means to correct them and of parameter optimization has emerged.

Stochastic Training of Neural Networks via Successive Convex Approximations

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of non-convex optimization, going under the general name of successive convex approximation (SCA) techniques. The basic idea is to iteratively replace the original (non-convex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Differently from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the neural network function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN's weights. We experiment on several medium-sized benchmark problems, and on a large-scale dataset involving simulated physical data. The results show how the algorithm outperforms state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.Comment: Preprint submitted to IEEE Transactions on Neural Networks and Learning System

CUB models: a preliminary fuzzy approach to heterogeneity

In line with the increasing attention paid to deal with uncertainty in ordinal data models, we propose to combine Fuzzy models with \cub models within questionnaire analysis. In particular, the focus will be on \cub models' uncertainty parameter and its interpretation as a preliminary measure of heterogeneity, by introducing membership, non-membership and uncertainty functions in the more general framework of Intuitionistic Fuzzy Sets. Our proposal is discussed on the basis of the Evaluation of Orientation Services survey collected at University of Naples Federico II.Comment: 10 pages, invited contribution at SIS2016 (Salerno, Italy), in SIS2016 proceeding

The Direction of Technical Change in Capital-Resource Economies

We analyze a multi-sector growth model with directed technical change where man-made capital and exhaustible resources are essen- tial for production. The relative profitability of factor-specific inno- vations endogenously determines whether technical progress will be capital- or resource-augmenting. We show that convergence to bal- anced growth implies zero capital-augmenting innovations: in the long run, the economy exhibits purely resource-augmenting technical change. This result provides sound microfoundations for the broad class of models of exogenous/endogenous growth where resource-aug- menting progress is required to sustain consumption in the long run, contradicting the view that these models are conceptually biased in favor of sustainability.Endogenous Growth, Directed Technical Change, Exhaustible Resources, Sustainability