An experimental study of coupling between combustor pressure, fuel/air mixing, and the flame

Fuel-air mixing behavior under the influence of imposed acoustic oscillations has been studied by investigating the response of the fuel mixture fraction field. The distribution of local fuel mixture fraction inside the mixing zone, which is expected to evolve into the local equivalence ratio in the flame zone, is closely coupled to unstable and oscillatory flame behavior. The Experiment was performed with an aerodynamically-stabilized non-premixed burner. In this study, acoustic oscillations were imposed at 22, 27, 32, 37, and 55Hz. Phase-resolved acetone PLIF was used to image the flow field of both isothermal and reacting flow cases and this data along with the derived quantities of temporal and spatial unmixedness were employed for analysis. The behavior of the unmixedness factor is compared with the previous measurements of oscillations in the flame zone. This comparison shows that local oscillations (of order millimeters or smaller) in fuel/air mixing are closely related to the oscillatory behavior of the flame. For each driving frequency, the mixture fraction oscillates at that frequency but with a slight phase difference between it and the pressure field/flame intensity, indicating that the fuel mixture fraction oscillation are likely the major reason for oscillatory behaviors of this category of flames and combustor geometry

Public goods and decay in networks

We propose a simple behavioral model to analyze situations where (1) a group of agents repeatedly plays a public goods game within a network structure and (2) each agent only observes the past behavior of her neighbors, but is affected by the decisions of the whole group. The model assumes that agents are imperfect conditional cooperators, that they infer unobserved contributions assuming imperfect conditional cooperation by others, and that they have some degree of bounded rationality. We show that our model approximates quite accurately regularities derived from public goods game experiments

The Bispectrum of IRAS Galaxies

We compute the bispectrum for the galaxy distribution in the IRAS QDOT, 2Jy, and 1.2Jy redshift catalogs for wavenumbers 0.05<k<0.2 h/Mpc and compare the results with predictions from gravitational instability in perturbation theory. Taking into account redshift space distortions, nonlinear evolution, the survey selection function, and discreteness and finite volume effects, all three catalogs show evidence for the dependence of the bispectrum on configuration shape predicted by gravitational instability. Assuming Gaussian initial conditions and local biasing parametrized by linear and non-linear bias parameters b_1 and b_2, a likelihood analysis yields 1/b_1 = 1.32^{+0.36}_{-0.58}, 1.15^{+0.39}_{-0.39} and b_2/b_1^2=-0.57^{+0.45}_{-0.30}, -0.50^{+0.31}_{-0.51}, for the for the 2Jy and 1.2Jy samples, respectively. This implies that IRAS galaxies trace dark matter increasingly weakly as the density contrast increases, consistent with their being under-represented in clusters. In a model with chi^2 non-Gaussian initial conditions, the bispectrum displays an amplitude and scale dependence different than that found in the Gaussian case; if IRAS galaxies do not have bias b_1> 1 at large scales, \chi^2 non-Gaussian initial conditions are ruled out at the 95% confidence level. The IRAS data do not distinguish between Lagrangian or Eulerian local bias.Comment: 30 pages, 11 figure

Gravity and Large-Scale Non-local Bias

The relationship between galaxy and matter overdensities, bias, is most often assumed to be local. This is however unstable under time evolution, we provide proofs under several sets of assumptions. In the simplest model galaxies are created locally and linearly biased at a single time, and subsequently move with the matter (no velocity bias) conserving their comoving number density (no merging). We show that, after this formation time, the bias becomes unavoidably non-local and non-linear at large scales. We identify the non-local gravitationally induced fields in which the galaxy overdensity can be expanded, showing that they can be constructed out of the invariants of the deformation tensor (Galileons). In addition, we show that this result persists if we include an arbitrary evolution of the comoving number density of tracers. We then include velocity bias, and show that new contributions appear, a dipole field being the signature at second order. We test these predictions by studying the dependence of halo overdensities in cells of fixed matter density: measurements in simulations show that departures from the mean bias relation are strongly correlated with the non-local gravitationally induced fields identified by our formalism. The effects on non-local bias seen in the simulations are most important for the most biased halos, as expected from our predictions. The non-locality seen in the simulations is not fully captured by assuming local bias in Lagrangian space. Accounting for these effects when modeling galaxy bias is essential for correctly describing the dependence on triangle shape of the galaxy bispectrum, and hence constraining cosmological parameters and primordial non-Gaussianity. We show that using our formalism we remove an important systematic in the determination of bias parameters from the galaxy bispectrum, particularly for luminous galaxies. (abridged)Comment: 26 pages, 9 figures. v2: improved appendix

The Power Spectrum, Bias Evolution, and the Spatial Three-Point Correlation Function

We calculate perturbatively the normalized spatial skewness, $S_3$, and full three-point correlation function (3PCF), $\zeta$, induced by gravitational instability of Gaussian primordial fluctuations for a biased tracer-mass distribution in flat and open cold-dark-matter (CDM) models. We take into account the dependence on the shape and evolution of the CDM power spectrum, and allow the bias to be nonlinear and/or evolving in time, using an extension of Fry's (1996) bias-evolution model. We derive a scale-dependent, leading-order correction to the standard perturbative expression for $S_3$ in the case of nonlinear biasing, as defined for the unsmoothed galaxy and dark-matter fields, and find that this correction becomes large when probing positive effective power-spectrum indices. This term implies that the inferred nonlinear-bias parameter, as usually defined in terms of the smoothed density fields, might depend on the chosen smoothing scale. In general, we find that the dependence of $S_3$ on the biasing scheme can substantially outweigh that on the adopted cosmology. We demonstrate that the normalized 3PCF, $Q$, is an ill-behaved quantity, and instead investigate $Q_V$, the variance-normalized 3PCF. The configuration dependence of $Q_V$ shows similarly strong sensitivities to the bias scheme as $S_3$, but also exhibits significant dependence on the form of the CDM power spectrum. Though the degeneracy of $S_3$ with respect to the cosmological parameters and constant linear- and nonlinear-bias parameters can be broken by the full configuration dependence of $Q_V$, neither statistic can distinguish well between evolving and non-evolving bias scenarios. We show that this can be resolved, in principle, by considering the redshift dependence of $\zeta$.Comment: 41 pages, including 12 Figures. To appear in The Astrophysical Journal, Vol. 521, #

Beyond-CMOS Artificial Neuron: A simulation-based exploration of the molecular-FET

The recent growth of Artificial Neural Networks fueled the design of numerous Artificial Intelligence (AI) dedicated hardware implementations. High power dissipation, computational complexity, and large area footprints currently limit CMOS based real-time embedded AI applications. In this work, we design and simulate through SPICE, for the first time, an artificial analog neuron based on the molecular Field-Effect Transistor (molFET) technology. MolFETs are described by a circuital model whose physical characteristics are extracted from atomistic simulations. The designed neuron is a single column of a crossbar-like circuit representing a layer of seven parallel neurons. The drain currents sum up in a soma-like circuit - modelled through a comparator - and trigger the output pulses. We demonstrate the advantages of the molFET in terms of area, power, and speed by comparing it with a conventional MOSFET implementation. The results confirm the molecular technology is a promising candidate for accomplishing high neuron throughput capability and massive redundancy, still providing high energy efficiency. The obtained results foster further investigation of molFET technology both at the device and circuit level

Constraints on primordial non-Gaussianity from WMAP7 and Luminous Red Galaxies power spectrum and forecast for future surveys

We place new constraints on the primordial local non-Gaussianity parameter f_NL using recent Cosmic Microwave Background anisotropy and galaxy clustering data. We model the galaxy power spectrum according to the halo model, accounting for a scale dependent bias correction proportional to f_NL/k^2. We first constrain f_NL in a full 13 parameters analysis that includes 5 parameters of the halo model and 7 cosmological parameters. Using the WMAP7 CMB data and the SDSS DR4 galaxy power spectrum, we find f_NL=171\pm+140 at 68% C.L. and -69<f_NL<+492 at 95% C.L.. We discuss the degeneracies between f_NL and other cosmological parameters. Including SN-Ia data and priors on H_0 from Hubble Space Telescope observations we find a stronger bound: -35<f_NL<+479 at 95% C.L.. We also fit the more recent SDSS DR7 halo power spectrum data finding, for a \Lambda-CDM+f_NL model, f_NL=-93\pm128 at 68% C.L. and -327<f_{NL}<+177 at 95% C.L.. We finally forecast the constraints on f_NL from future surveys as EUCLID and from CMB missions as Planck showing that their combined analysis could detect f_NL\sim 5.Comment: 10 pages, 5 figures, 3 table

PPFL: privacy-preserving federated learning with trusted execution environments

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54x) and a similar amount of network traffic (1.002x) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side

Nonlinearity and stochasticity in the density--velocity relation

We present results of the investigations of the statistical properties of a joint density and velocity divergence probability distribution function (PDF) in the mildly non-linear regime. For that purpose we use both perturbation theory results, extended here for a top-hat filter, and numerical simulations. In particular we derive the quantitative (complete as possible up to third order terms) and qualitative predictions for constrained averages and constrained dispersions -- which describe the nonlinearities and the stochasticity properties beyond the linear regime -- and compare them against numerical simulations. We find overall a good agreement for constrained averages; however, the agreement for constrained dispersions is only qualitative. Scaling relations for the Omega-dependence of these quantities are satisfactory reproduced. Guided by our analytical and numerical results, we finally construct a robust phenomenological description of the joint PDF in a closed analytic form. The good agreement of our formula with results of N-body simulations for a number of cosmological parameters provides a sound validation of the presented approach. Our results provide a basis for a potentially powerful tool with which it is possible to analyze galaxy survey data in order to test the gravitational instability paradigm beyond the linear regime and put useful constraints on cosmological parameters. In particular we show how the nonlinearity in the density--velocity relation can be used to break the so-called Omega-bias degeneracy in cosmic density-velocity comparisons.Comment: 12 pages, 11 figures; revised version with minor changes in the presentation, accepted for publication in MNRA