The Complex Network of Evolutionary Computation Authors: an Initial Study

EC paper authors form a complex network of co-authorship which is, by itself, a example of an evolving system with its own rules, concept of fitness, and patterns of attachment. In this paper we explore the network of authors of evolutionary computation papers found in a major bibliographic database. We examine its macroscopic properties, and compare it with other co-authorship networks; the EC co-authorship network yields results in the same ballpark as other networks, but exhibits some distinctive patterns in terms of internal cohesion. We also try to find some hints on what makes an author a sociometric star. Finally, the role of proceeding editorship as the origin of long-range links in the co-authorship network is studied as well.Comment: Sociometric study of the Evolutionary Computation community. Submitted to Evolutionary Computation lette

Aspects of emergent geometry in the AdS/CFT context

We study aspects of emergent geometry for the case of orbifold superconformal field theories in four dimensions, where the orbifolds are abelian within the AdS/CFT proposal. In particular, we show that the realization of emergent geometry starting from the N=4 SYM theory in terms of a gas of particles in the moduli space of vacua of a single D3 brane in flat space gets generalized to a gas of particles on the moduli space of the corresponding orbifold conformal field theory (a gas of D3 branes on the orbifold space). Our main purpose is to show that this can be analyzed using the same techniques as in the N=4 SYM case by using the method of images, including the measure effects associated to the volume of the gauge orbit of the configurations. This measure effect gives an effective repulsion between the particles that makes them condense into a non-trivial vacuum configuration, and it is exactly these configurations that lead to the geometry of X in the AdS x X dual field theoryComment: 24 page

Image Processing of Temperature Fields from Infrared Termography of Micro-Mixers with Polymeric Substrates

International audienceThe present work deals with the image processing and thermal analysis of micro-mixers from the data provided by an infrared camera thermography system. The micro-mixers are prepared by photolitography on a polymeric substrate and the camera employed is the FLIR SC645 with the proprietary software ThermaCam Researcher Pro v2.10. The thermal analysis is aimed at understanding the direct contact heat transfer between two fluid streams and the polymeric substrate at different inlet temperatures and mass flow rates, within mixers of various geometric configurations. Infrared thermography is thus employed to measure the external wall temperatures fields along the mixer length. Water at different inlet temperatures has been used as the working fluid in all cases and the mass flow rates of the two streams have been imposed through independent syringe pumps. The image processing and analysis of the experimental results show the basic qualitative and quantitative features of the heat transfer phenomena and indicates that a conjugated heat transfer formulation of the micro-mixer structure should be pursued for accurate quantitative analysis in theoretical predictions

Probabilistic Invariant Learning with Randomized Linear Classifiers

Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and design models that are both expressive and invariant but use less resources. Inspired by randomized algorithms, our key insight is that accepting probabilistic notions of universal approximation and invariance can reduce our resource requirements. More specifically, we propose a class of binary classification models called Randomized Linear Classifiers (RLCs). We give parameter and sample size conditions in which RLCs can, with high probability, approximate any (smooth) function while preserving invariance to compact group transformations. Leveraging this result, we design three RLCs that are provably probabilistic invariant for classification tasks over sets, graphs, and spherical data. We show how these models can achieve probabilistic invariance and universality using less resources than (deterministic) neural networks and their invariant counterparts. Finally, we empirically demonstrate the benefits of this new class of models on invariant tasks where deterministic invariant neural networks are known to struggle

Numerical tests of AdS/CFT at strong coupling

We study various correlation functions (two and three point functions) in a large $N$ matrix model of six commuting matrices with a numerical Monte Carlo algorithm. This is equivalent to a model of a gas of particles in six dimensions with a confining quadratic potential and logarithmic repulsions at finite temperature, where we are measuring the leading order non-gaussianities in the thermal fluctuations. This is a simplified model of the low energy dynamics of N=4 SYM at strong coupling. We find strong evidence that the simplified matrix model matches with the dual gravitational description of three point functions in the AdS/CFT correspondence.Comment: 23 pages, 7 figures, revtex. v2: minor correction

Enhanced Eshelby twist on thin wurtzite InP nanowires and measurement of local crystal rotation

We have performed a detailed study of the lattice distortions of InP wurtzite nanowires containing an axial screw dislocation. Eshelby predicted that this kind of system should show a crystal rotation due to the dislocation induced torque. We have measured the twisting rate and the dislocation Burgers vector on individual wires, revealing that nanowires with a 10-nm radius have a twist up to 100% larger than estimated from elasticity theory. The strain induced by the deformation has a Mexican-hat-like geometry, which may create a tube-like potential well for carriers