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The proceeding at: 18th International Conference on Parallel and Distributed Computing, Euro-Par 2012), took place 2012, August 27-31, in Rhodes Island, Greece. This work considers Internet-based task computations in which a master process assigns tasks, over the Internet, to rational workers and collect their responses. The objective is for the master to obtain the correct task outcomes. For this purpose we formulate and study the dynamics of evolution of Internet-based master-worker computations through reinforcement learning. This work is supported by the Cyprus Research Promo-tion Foundation grant TΠE/ΠΛHPO/0609(BE)/05, NSF grants CCF-0937829, CCF-1114930, Comunidad de Madrid grant S2009TIC-1692, Spanish MOSAICO and RESINEE grants and MICINN grant TEC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002. Publicado

Prestressed concrete bridges currently account for 45% of bridges built in the last 5 years in the United States. This has resulted in an increase in the number of deficient bridges composed of prestressed concrete, which requires a better understanding of the on-site performance of this building material. The use of new materials, such as high-performance concrete, in conjunction with prestressing provides additional motivation for the creation of structural health monitoring methods for prestressed concrete. This paper identifies two parameters relevant to prestressed concrete, along with methods for their evaluation. The parameters evaluated are the prestressing force value at transfer and the width of prerelease cracks, both of which are indicators of structural performance. Improper transfer of the prestressing force can result in tensile stresses in the concrete that exceed capacity and result in cracks and/or excessive deflections. Prerelease cracks occur in the concrete prior to transfer of the prestressing force and are mainly caused by autogenous shrinkage and thermal gradients. Closure of the cracks is expected by virtue of prestressing force transfer. However, the extent of crack closure is important in order to guarantee durability and structural integrity. This paper presents an integral overview of two novel methods for the statistical evaluation of the two monitored parameters: prestressing forces and the width of prerelease cracks. Validation of the methods is performed through application to two structures, both of which are components of Streicker Bridge on the Princeton University campus. Uncertainties are evaluated and thresholds for unusual behavior are set through the application.

This repository provides a recipe that showcases how to read and process native model output with ESMValTool. Running this recipe produces Figures 2–7 of Schlund et al., GMDD (2022), https://doi.org/10.5194/gmd-2022-205. The recipe can easily be expanded to use more complex diagnostics. In its current form, simple plots useful for monitoring climate model simulations are generated. Extensive documentation of this feature is provided here: https://docs.esmvaltool.org/en/latest/input.html#datasets-in-native-format Prerequisites: ESMValTool: v2.6.0 or later (https://www.esmvaltool.org/). Input data: ERA5 reanalysis data and native model output from the Earth system models CESM2, EC-Earth3, EMAC, ICON, and IPSL-CM6.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

This is the initial release, it corresponds to the time the NSF final report for the project was submitted and approved.

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

Vague impact criteria are a blessing in disguise. Researchers who push against criteria that allow considerable autonomy are foolish and should learn from overseas contemporaries that a clearer definition of impact requirements is not dissimiliar from a tightening of the noose, write J. Britt Holbrook and Robert Frodeman.

PYTHON code for measuring seismic travel-time changes with the wavelet method Contact: Han Byul Woo (hanbyulwoo@gmail.com) This PYTHON script package contains codes and test data for plotting quality of dispersion measurements obtained from two ambient-noise data processing methods. Phase cross-correlation and phase-weighted stacking Time cross-correlation and linear stacking PYTHON version 3.9 was used to run the script and following packages are required to run the scripts. 1.numpy 2.scipy 3.csv 4.sklearn 5.matplotlib Table of contents: — quality_control_aux.py: Core functions to plot quality parameters and quality controlled group velocity curves. The script also includes estimation of reproducibility and find the number of progressive stacks required to have a root-mean-squared error value away from a selected reference network-averaged group velocity curve. — plot_quality_control.py: Loads dispersion measurements and quality (signal-to-noise ratio and number of wavelengths) to plot the quality and quality controlled group velocity curves. — Dispersion_Data: Includes dispersion meassurements and quality for each wave period calculated based on two data processing methods (PCC-PWS and Time-Lin). Dispersion measurements for each progressive stacks are also included to estimate the reproducibility.

We study the complexity of approximation for a weighted counting constraint satisfaction problem #CSP(F). In the conservative case, where F contains all unary functions, a classification is known for the Boolean domain. We give a classification for problems with general finite domain. We define weak log-modularity and weak log-supermodularity, and show that #CSP(F) is in FP if F is weakly log-modular. Otherwise, it is at least as hard to approximate as #BIS, counting independent sets in bipartite graphs, which is believed to be intractable. We further sub-divide the #BIS-hard case. If F is weakly log-supermodular, we show that #CSP(F) is as easy as Boolean log-supermodular weighted #CSP. Otherwise, it is NP-hard to approximate. Finally, we give a trichotomy for the arity-2 case. Then, #CSP(F) is in FP, is #BIS-equivalent, or is equivalent to #SAT, the problem of approximately counting satisfying assignments of a CNF Boolean formula.