Phenomenology of TMDs

We present a review of current Transverse Momentum Dependent (TMD) phenomenology focusing our attention on the unpolarized TMD parton distribution function and the Sivers function. The paper introduces and comments about the new Collins-Soper-Sterman (CSS) TMD evolution formalism. We make use of a selection of results obtained by several groups to illustrate the achievements and the failures of the simple Gaussian approach and the TMD CSS evolution formalism.Comment: 10 pages, 5 figures, to appear in the proceedings of the TRANSVERSITY 2014, Chia, Italy, 9-13 June, 201

Black hole solutions of dimensionally reduced Einstein-Gauss-Bonnet gravity with a cosmological constant

We study the phase space of the spherically symmetric solutions of the system obtained from the dimensional reduction of the six-dimensional Einstein-Gauss-Bonnet action with a cosmological constant. We show that all the physical solutions have anti-de Sitter asymptotic behavior.Comment: 13 pages, plain Te

Planning and Proof Planning

. The paper adresses proof planning as a specific AI planning. It describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning. 1 Introduction Planning is an established area of Artificial Intelligence (AI) whereas proof planning introduced by Bundy in [2] still lives in its childhood. This means that the development of proof planning needs maturing impulses and the natural questions arise What can proof planning learn from its Big Brother planning?' and What are the specific characteristics of the proof planning domain that determine the answer?'. In turn for planning, the analysis of approaches points to a need of mature techniques for practical planning. Drummond [8], e.g., analyzed approaches with the conclusion that the success of Nonlin, SIPE, and O-Plan in practical planning can be attributed to hierarchical action expansion, the explicit representation of a plan's causal structure, and a very simple form of propo..

COMGEN: A computer program for generating finite element models of composite materials at the micro level

COMGEN (Composite Model Generator) is an interactive FORTRAN program which can be used to create a wide variety of finite element models of continuous fiber composite materials at the micro level. It quickly generates batch or session files to be submitted to the finite element pre- and postprocessor PATRAN based on a few simple user inputs such as fiber diameter and percent fiber volume fraction of the composite to be analyzed. In addition, various mesh densities, boundary conditions, and loads can be assigned easily to the models within COMGEN. PATRAN uses a session file to generate finite element models and their associated loads which can then be translated to virtually any finite element analysis code such as NASTRAN or MARC

Flaw-tolerance in silk fibrils explains strength, extensibility and toughness of spider silk

Silk is an ancient but remarkably strong, extensible and tough material made from simple protein building blocks. Earlier work has shown that the particular molecular geometry of silk with a composite of semi-amorphous and nanocrystalline beta-sheet protein domains provides the structural basis for its characteristic softening-stiffening behavior and remarkable strength at the nanoscale. Yet, an open question remains as to how these nanoscale properties are upscaled so effectively to create strong, extensible and tough silk fibers. Here we discover that the geometric confinement of fibrils to ≈50-100 nm width and arranged in bundles to form larger-scale silk fibers, is the key to explaining the upscaling of the mechanical properties of silk from the atomistic scale upwards. We find that under this geometric confinement, hundreds of thousands of protein domains unfold simultaneously and thereby act synergistically to resist deformation and failure, providing access to enhanced large-scale strength, extensibility and toughness. Moreover, since the material is in a flaw-tolerant state under this geometric confinement, structural inhomogeneities such as cavities or tears that typically act as stress concentrators do not compromise the material performance. Indeed, experimental work showed that the diameter of silk fibrils that make up larger-scale silk fibers are on the order of 20-100 nm, in agreement with our findings. The exploitation of this mechanism in engineering design enables the synthesis of hierarchical fiber materials for superior performance despite limited and inferior building blocks

Do optimization methods in deep learning applications matter?

With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention. Several implementations favor the use of Conjugate Gradient (CG) and Stochastic Gradient Descent (SGD) as being practical and elegant solutions to achieve quick convergence, however, these optimization processes also present many limitations in learning across deep learning applications. Recent research is exploring higher-order optimization functions as better approaches, but these present very complex computational challenges for practical use. Comparing first and higher-order optimization functions, in this paper, our experiments reveal that Levemberg-Marquardt (LM) significantly supersedes optimal convergence but suffers from very large processing time increasing the training complexity of both, classification and reinforcement learning problems. Our experiments compare off-the-shelf optimization functions(CG, SGD, LM and L-BFGS) in standard CIFAR, MNIST, CartPole and FlappyBird experiments.The paper presents arguments on which optimization functions to use and further, which functions would benefit from parallelization efforts to improve pretraining time and learning rate convergence