Algon: a framework for supporting comparison of distributed algorithm performance

Programmers often need to use distributed algorithms to add non-functional behaviour such as mutual exclusion, deadlock detection and termination, to a distributed application. They find the selection and implementation of these algorithms daunting. Consequently, they have no idea which algorithm will be best for their particular application. To address this difficulty the Algon framework provides a set of pre-coded distributed algorithms for programmers to choose from, and provides a special performance display tool to support choice between algorithms. The performance tool is discussed. The developer of a distributed application will be able to observe the performance of each of the available algorithms according to a set of of widely accepted and easily-understandable performance metrics and compare and contrast the behaviour of the algorithms to support an informed choice. The strength of the Algon framework is that it does not require a working knowledge of algorithmic theory or functionality in order for the developer to use the algorithms

The impact of stellar feedback on the density and velocity structure of the interstellar medium

We study the impact of stellar feedback in shaping the density and velocity structure of neutral hydrogen (HI) in disc galaxies. For our analysis, we carry out $\sim 4.6$pc resolution $N$-body+adaptive mesh refinement (AMR) hydrodynamic simulations of isolated galaxies, set up to mimic a Milky Way (MW), and a Large and Small Magellanic Cloud (LMC, SMC). We quantify the density and velocity structure of the interstellar medium using power spectra and compare the simulated galaxies to observed HI in local spiral galaxies from THINGS (The HI Nearby Galaxy Survey). Our models with stellar feedback give an excellent match to the observed THINGS HI density power spectra. We find that kinetic energy power spectra in feedback regulated galaxies, regardless of galaxy mass and size, show scalings in excellent agreement with super-sonic turbulence ($E(k)\propto k^{-2}$) on scales below the thickness of the HI layer. We show that feedback influences the gas density field, and drives gas turbulence, up to large (kpc) scales. This is in stark contrast to density fields generated by large scale gravity-only driven turbulence. We conclude that the neutral gas content of galaxies carries signatures of stellar feedback on all scales.Comment: 19 pages, 13 figures, 2 tables, accepted for publication in Monthly Notices of the Royal Astronomical Societ

Decoherence and entropy of primordial fluctuations II. The entropy budget

We calculate the entropy of adiabatic perturbations associated with a truncation of the hierarchy of Green functions at the first non trivial level, i.e. in a self-consistent Gaussian approximation. We give the equation governing the entropy growth and discuss its phenomenology. It is parameterized by two model-dependent kernels. We then examine two particular inflationary models, one with isocurvature perturbations, the other with corrections due to loops of matter fields. In the first model the entropy grows rapidely, while in the second the state remains pure (at one loop).Comment: 28 page

Nanosecond channel-switching exact optical frequency synthesizer using an optical injection phase-locked loop (OIPLL)

Experimental results are reported on an optical frequency synthesizer for use in dynamic dense wavelength-division-multiplexing networks, based on a tuneable laser in an optical injection phase-locked loop for rapid wavelength locking. The source combines high stability (50 dB), narrow linewidth (10 MHz), and fast wavelength switching (<10 ns)

A learning rule balancing energy consumption and information maximization in a feed-forward neuronal network

Information measures are often used to assess the efficacy of neural networks, and learning rules can be derived through optimization procedures on such measures. In biological neural networks, computation is restricted by the amount of available resources. Considering energy restrictions, it is thus reasonable to balance information processing efficacy with energy consumption. Here, we studied networks of non-linear Hawkes neurons and assessed the information flow through these networks using mutual information. We then applied gradient descent for a combination of mutual information and energetic costs to obtain a learning rule. Through this procedure, we obtained a rule containing a sliding threshold, similar to the Bienenstock-Cooper-Munro rule. The rule contains terms local in time and in space plus one global variable common to the whole network. The rule thus belongs to so-called three-factor rules and the global variable could be related to a number of biological processes. In neural networks using this learning rule, frequent inputs get mapped onto low energy orbits of the network while rare inputs aren't learned

Magnetoresistance and collective Coulomb blockade in super-lattices of ferromagnetic CoFe nanoparticles

We report on transport properties of millimetric super-lattices of CoFe nanoparticles surrounded by organic ligands. R(T)s follow R(T) = R_0.exp(T/T_0)^0.5 with T_0 ranging from 13 to 256 K. At low temperature I(V)s follow I=K[(V-V_T)/V_T]^ksi with ksi ranging 3.5 to 5.2. I(V) superpose on a universal curve when shifted by a voltage proportional to the temperature. Between 1.8 and 10 K a high-field magnetoresistance with large amplitude and a strong voltage-dependence is observed. Its amplitude only depends on the magnetic field/temperature ratio. Its origin is attributed to the presence of paramagnetic states present at the surface or between the nanoparticles. Below 1.8 K, this high-field magnetoresistance abruptly disappears and inverse tunnelling magnetoresistance is observed, the amplitude of which does not exceed 1%. At this low temperature, some samples display in their I(V) characteristics abrupt and hysteretic transitions between the Coulomb blockade regime and the conductive regime. The increase of the current during these transitions can be as high as a factor 30. The electrical noise increases when the sample is near the transition. The application of a magnetic field decreases the voltage at which these transitions occur so magnetic-field induced transitions are also observed. Depending on the applied voltage, the temperature and the amplitude of the magnetic field, the magnetic-field induced transitions are either reversible or irreversible. These abrupt and hysteretic transitions are also observed in resistance-temperature measurements. They could be the soliton avalanches predicted by Sverdlov et al. [Phys. Rev. B 64, 041302 (R), 2001] or could also be interpreted as a true phase transition between a Coulomb glass phase to a liquid phase of electrons