Measurement of complex fragments and clues to the entropy production from 42-137-MeV/nucleon Ar + Au

Intermediate-rapidity fragments with A=1-14 emitted from 42-137-MeV/nucleon Ar + Au have been measured. Evidence is presented that these fragments arise from a common moving source. Entropy values are extracted from the mass distributions by use of quantum statistical and Hauser-Feshbach theories. The extracted entropy values of S/A≈2-2.4 are much smaller than the values expected from measured deuteron-to-proton ratios, but are still considerably higher than theoretically predicted values

Light particle spectra from 35 MeV/nucleon 12C-induced reactions on 197Au

Energy spectra for p, d, t, 3He, 4He, and 6He from the reaction 12C+197Au at 35 MeV/nucleon are presented. A common intermediate rapidity source is identified using a moving source fit to the spectra that yields cross sections which are compared to analogous data at other bombarding energies and to several different models. The excitation function of the composite to proton ratios is compared with quantum statistical, hydrodynamic, and thermal models

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

© 1989-2012 IEEE. In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from most algorithms that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate O(1/T2O(1/T2). Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha

Investment Opportunities Forecasting: Extending the Grammar of a GP-based Tool

In this paper we present a new version of a GP financial forecasting tool, called EDDIE 8. The novelty of this version is that it allows the GP to search in the space of indicators, instead of using pre-specified ones. We compare EDDIE 8 with its predecessor, EDDIE 7, and find that new and improved solutions can be found. Analysis also shows that, on average, EDDIE 8's best tree performs better than the one of EDDIE 7. The above allows us to characterize EDDIE 8 as a valuable forecasting tool

The Microscopic Approach to Nuclear Matter and Neutron Star Matter

We review a variety of theoretical and experimental investigations aimed at improving our knowledge of the nuclear matter equation of state. Of particular interest are nuclear matter extreme states in terms of density and/or isospin asymmetry. The equation of state of matter with unequal concentrations of protons and neutrons has numerous applications. These include heavy-ion collisions, the physics of rare, short-lived nuclei and, on a dramatically different scale, the physics of neutron stars. The "common denominator" among these (seemingly) very different systems is the symmetry energy, which plays a crucial role in both the formation of the neutron skin in neutron-rich nuclei and the radius of a neutron star (a system 18 orders of magnitude larger and 55 orders of magnitude heavier). The details of the density dependence of the symmetry energy are not yet sufficiently constrained. Throughout this article, our emphasis will be on the importance of adopting a microscopic approach to the many-body problem, which we believe to be the one with true predictive power.Comment: 56 pages, review article to appear in the International Journal of Modern Physics